Analysis flow

Analysis type determines the relevant column lists and test design. Comparison-test items carry their own sample-size and distribution prerequisites, and results are interpreted with the selected significance threshold.

Description

Activation value of the statistics definition. Record and list services carry the field, while automatic-analysis selection separately checks parameter 40. The inspected code contains no shared backend check proving that passive blocks every manual execution endpoint, so status alone must not be treated as a guarantee that execution is prevented.

Options

Example

Description

Client identifier connecting the statistics definition to its owning Wanalyzer project. With the correct identifier, the definition appears in the project statistics list and automatic-analysis scan; an incorrect identifier creates a relation under another project context.

Example

Description

Name displayed for the analysis definition in lists and result screens. A descriptive and unique name distinguishes different test designs that use the same columns.

Example

Description

Note storing the purpose, population, and interpretation assumptions of the analysis. Code saves and returns it; a sufficient note clarifies which question the results answer.

Example

Description

Determines which statistical design group is used. Changing it makes the frontend display related fields such as descriptive columns, comparison tests, or before-and-after mappings; the design remains incomplete when lists required by the selected type are empty.

Options

Example

Description

Determines whether descriptive-analysis columns come from a manual list or are selected collectively by column type. Manual selection uses parameter 6; collective modes rely on column metadata.

Options

Example

Description

List containing column mappings used in descriptive analysis. Each item carries parameter 7, the column index, together with parameter 8, the usage type; indexes must be verified when column order changes.

Example

Description

Zero-based index of the project column used in the descriptive role. When the index matches the current column list, the intended data is analyzed; after columns are reordered, the mapping must be updated to avoid selecting a different column.

Example

Description

Determines whether the related column type is inferred automatically or forced to categorical or numeric. When forced, test selection and data preparation follow that role; a choice incompatible with the actual data may cause analysis failure or meaningless results.

Options

Example

Description

P-value threshold used for statistical significance; the frontend defines 0.05 as the default. Because result p values are compared with this threshold, a smaller value produces a stricter and a larger value a more permissive significance decision.

Example

Description

Stores whether the comparison is designed as multidimensional. Yes declares a design that considers multiple variable dimensions together; actual applicability must be evaluated with the selected test and column lists.

Options

Example

Description

List containing independent variable columns. Each item stores a column index and usage type; list order and indexes directly determine analysis input.

Example

Description

Zero-based index of the project column used in the independent-variable role. When the index matches the current column list, the intended data is analyzed; after columns are reordered, the mapping must be updated to avoid selecting a different column.

Example

Description

Determines whether the related column type is inferred automatically or forced to categorical or numeric. When forced, test selection and data preparation follow that role; a choice incompatible with the actual data may cause analysis failure or meaningless results.

Options

Example

Description

List containing dependent variable columns. Each item stores a column index and usage type, and may additionally contain a categorical target category; list order and indexes directly determine analysis input.

Example

Description

Zero-based index of the project column used in the dependent-variable role. When the index matches the current column list, the intended data is analyzed; after columns are reordered, the mapping must be updated to avoid selecting a different column.

Example

Description

Determines whether the related column type is inferred automatically or forced to categorical or numeric. When forced, test selection and data preparation follow that role; a choice incompatible with the actual data may cause analysis failure or meaningless results.

Options

Example

Description

Option value treated as the success or event category for a categorical dependent variable. Particularly in logistic regression, selecting the wrong target category may make the model explain the opposite event.

Example

Description

List containing automatic-comparison variable columns. Each item stores a column index and usage type; list order and indexes directly determine analysis input.

Example

Description

Zero-based index of the project column used in the automatic-comparison role. When the index matches the current column list, the intended data is analyzed; after columns are reordered, the mapping must be updated to avoid selecting a different column.

Example

Description

Determines whether the related column type is inferred automatically or forced to categorical or numeric. When forced, test selection and data preparation follow that role; a choice incompatible with the actual data may cause analysis failure or meaningless results.

Options

Example

Description

List containing comparison-test definitions. Each item combines a test name with pre-analysis sample-size, distribution, and independent-variable usage checks, allowing different tests to run under different eligibility rules.

Example

Description

Selects the statistical test in a comparison-list item. Test assumptions must match categorical or numeric column roles; an incompatible test may fail or produce an uninterpretable result.

Options

Example

Description

Determines which sample-size and distribution checks, and which AND/OR combination, are required before a test runs. The selection reveals dependent threshold and distribution fields; when the condition fails, the test is intended not to be used.

Options

Example

Description

Determines which comparison operator tests the actual sample count against the configured threshold. Reversing the operator may reject sufficient data or accept insufficient data.

Options

Example

Description

Sample-count threshold required for the total. It is meaningful only when the corresponding pre-analysis check type is selected; use a positive value appropriate to the study design.

Example

Description

Determines which comparison operator tests the actual sample count against the configured threshold. Reversing the operator may reject sufficient data or accept insufficient data.

Options

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Description

Sample-count threshold required for each group. It is meaningful only when the corresponding pre-analysis check type is selected; use a positive value appropriate to the study design.

Example

Description

Determines how results of multiple distribution checks are combined. One, all, or all-not-normal choices produce different eligibility decisions from the same test results.

Options

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Description

Determines whether the related distribution measure or test participates in pre-analysis evaluation. With yes, its result is included under the combination rule in parameter 28; with no, it does not contribute to the decision.

Options

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Description

Acceptance threshold for skewness. Unless the related usage field is yes, the value does not participate in the decision; widening the threshold accepts more distributions.

Example

Description

Determines whether the related distribution measure or test participates in pre-analysis evaluation. With yes, its result is included under the combination rule in parameter 28; with no, it does not contribute to the decision.

Options

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Description

Acceptance threshold for kurtosis. Unless the related usage field is yes, the value does not participate in the decision; widening the threshold accepts more distributions.

Example

Description

Determines whether the related distribution measure or test participates in pre-analysis evaluation. With yes, its result is included under the combination rule in parameter 28; with no, it does not contribute to the decision.

Options

Example

Description

Determines whether the related distribution measure or test participates in pre-analysis evaluation. With yes, its result is included under the combination rule in parameter 28; with no, it does not contribute to the decision.

Options

Example

Description

Determines whether multiple independent variables are evaluated separately, jointly, or in two stages. The two-stage choice carries variables significant in individual analysis into a joint model.

Options

Example

Description

List containing before-and-after analysis pairs. Each item contains a test name and before and after column indexes belonging to the same observations; using unpaired columns violates the paired-test assumption.

Example

Description

Selects the paired test applied to the before and after column pair. The current option is the paired-samples t test and requires numeric, matched observations.

Options

Example

Description

Zero-based index of the project column used in the before role. When the index matches the current column list, the intended data is analyzed; after columns are reordered, the mapping must be updated to avoid selecting a different column.

Example

Description

Zero-based index of the project column used in the after role. When the index matches the current column list, the intended data is analyzed; after columns are reordered, the mapping must be updated to avoid selecting a different column.

Example

Description

Determines whether this statistics definition becomes eligible for automatic execution after project data preparation. AutomaticAnalyzeProcess includes the definition only when the value is yes; with no, manual execution may remain available but no post-preparation automatic trigger occurs.

Options

Example

Description

Stores the request to send email after analysis, and the frontend displays mail fields 42 through 58 when it is yes. No implementation using these fields to send email was found in the inspected Wanalyzer backend scope, so delivery may depend on another service.

Options

Example

Description

Lists file formats requested as analysis-email attachments. Excel and PDF may both be selected; record and get services carry the field, but attachment generation and delivery were not verified in the inspected backend scope.

Options

Example

Description

Stores the analysis email’s recipient addresses. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

Stores the analysis email’s displayed sender name. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s subject prefix. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

Stores the analysis email’s subject suffix. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s message introduction. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

Stores the analysis email’s repeatable content blocks. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s content caption. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

Stores the analysis email’s content text. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s content color. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s background image. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s table-border color. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

Stores the analysis email’s table-background color. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s signature blocks. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

Stores the analysis email’s signature note. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

Stores the analysis email’s signature image. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

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Description

Stores the analysis email’s signature-image width. Statistics record and get services preserve the field; because its interpretation during email rendering was not verified in the inspected Wanalyzer backend scope, format, color, or dimension values must match the target mail service.

Example

Description

JSON rule restricting dataset rows included in the analysis by column conditions. The frontend rule editor produces query_list and compilation arrays, the record service serializes the object to JSON text, and the get service parses it back; manually entered invalid JSON may prevent the definition from loading.

Example

Description

Enables or disables automatic generation of variable combinations for comparison analysis. The frontend displays it only for comparison designs; active carries the request to generate combinations from column lists, while the number of combinations depends on selected columns.

Options

Example

Process flow

When analysis is triggered, the process is created with start status and zero counters. Start and continue are actively tracked; cancel and complete end tracking and refresh results.

Description

Lifecycle status of the statistical analysis process. The record is created with start when analysis is triggered. When start or continue is selected, the process is treated as active and the interface refreshes it every five seconds; when cancel or complete is selected, tracking stops and the result list is reloaded.

Options

Example

Description

Client identifier connecting the process to its owning Wanalyzer project. With the correct project identifier, the active-process service reports the record under the related project and result authorization is evaluated in the correct project context; an incorrect identifier breaks the process-to-project relationship.

Example

Description

Client identifier of the statistics definition that produced the process. The process list is read under the selected statistics record through this relation; the correct identifier associates generated results with the correct analysis design, while an incorrect identifier may make them appear under another statistics definition.

Example

Description

Start time at which the analysis process is created. The trigger service fills this field in Coordinated Universal Time with second precision; it is therefore used to order processes chronologically and to calculate approximate execution duration together with the end date.

Example

Description

Time at which analysis execution ends. When populated after process completion, it defines the execution interval together with the start date; it may remain empty while the process is still running or when a cancelled record has not produced an end time.

Example

Description

Shows when the running process most recently reported progress. It is initialized to the same value as the start date and the active-process service returns it together with status; regular updates indicate process activity, while a value that remains unchanged for a long time may indicate a delayed or stalled process.

Example

Description

Stores the time at which the process was stopped or cancelled. When populated together with cancel status, it explains when execution was terminated; a value in this field alone does not stop interface tracking because the tracking decision depends on status being cancel or complete.

Example

Description

Counter holding the number of dataset rows associated with or processed by the analysis process. It is initialized to zero when the process record is created; when the analysis engine updates it, it indicates scope and progress, while remaining zero does not by itself mean failure because the process may have only just started.

Example

Description

Counter holding the total number of result records produced by the process. It is initialized to zero when the record is created; when updated as results are written to the child result layer, it allows the user to see the expected result volume. This field is not the result content; details are read from the process-result layer.

Example

Description

Text carrying the process launch mode, progress notes, or error descriptions. At creation, the trigger service writes an Analyze entry identifying authorization or credit execution mode; when subsequent steps append details, this field can explain why the operation completed, was delayed, or was cancelled.

Example

Result flow

Analysis and test type determine field interpretation; the p value is compared with the significance threshold; raw JSON fields generate tables and distribution diagnostics.

Description

Activation status of the result record. Active marks the record as suitable for use as a current result, while passive marks it as historical or out of use. The inspected list service returns child records without applying a status filter, so this value alone must not be assumed to hide the record from lists.

Options

Example

Description

Client identifier connecting the result record to the related project. With the correct identifier, the result is found in the correct hierarchy; an incorrect identifier may direct authorization, listing, or historical-result selection to a different context.

Example

Description

Client identifier connecting the result record to the related statistics definition. With the correct identifier, the result is found in the correct hierarchy; an incorrect identifier may direct authorization, listing, or historical-result selection to a different context.

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Description

Client identifier connecting the result record to the related statistics process. With the correct identifier, the result is found in the correct hierarchy; an incorrect identifier may direct authorization, listing, or historical-result selection to a different context.

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Description

Time when result generation started. Together, start and end dates allow execution duration to be calculated.

Example

Description

Time when result generation finished. When evaluated with the start date, the end date gives execution duration; if empty, the result may be unfinished or the date may not have been recorded.

Example

Description

Determines which analysis design produced the result. Because it controls how the interface interprets related fields such as descriptive columns, comparison variables, before-and-after mappings, or the Cronbach alpha measure, it must agree with the result content.

Options

Example

Description

Identifies the statistical test that produced a comparative result. The interface prioritizes median and interquartile range for nonparametric Kruskal–Wallis and Mann–Whitney U results, and mean and standard deviation for other comparisons, so the correct test code directly affects presentation.

Options

Example

Description

Number of valid observations used to calculate the result. It may serve as the denominator for percentages and table totals; when empty or zero in older records, the interface uses the sum of counts in frequency JSON as a fallback denominator.

Example

Description

Calculated p value of the statistical test. The interface compares it with the significance threshold in parameter 10 and highlights the result as significant when the p value is smaller. Values below 0.001 are displayed as “<0.001”.

Example

Description

P-value threshold used to decide whether the result is significant. If the field cannot be converted to a number, test-selection assessment uses 0.05; a smaller threshold gives a stricter decision and a larger threshold gives a more permissive decision.

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Description

Explanation, warning, or error note produced by the analysis engine. The interface carries this field as the result note in categorical-to-numeric group tables, so calculation limitations or reasons for missing data should be stated clearly here.

Example

Description

List carrying interpretation text for the same result in multiple languages. Each item stores parameter 13 content together with parameter 14 language; when an item matching the interface language exists, the user sees a readable interpretation together with calculated values.

Example

Description

Readable interpretation content in a result-text item. Explaining the p value or effect direction in context reduces the risk of misinterpreting the raw number; content that does not match its language field may be presented under the wrong language.

Example

Description

Language code of a result-text item. Selecting Turkish or English determines the language view in which the content is used; an incorrect code may prevent the interface from selecting the correct text.

Options

Example

Description

Zero-based index of the project column used in the descriptive role. Because the interface resolves the column name and options from this index, reordering project columns later may associate an old result with a different column or make the table skip it.

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Description

List carrying independent variable columns used in the result. Each item contains its column index; because axis and group names for categorical-to-categorical and categorical-to-numeric tables are produced from these mappings, the list must represent the same roles as the result JSON structure.

Example

Description

Zero-based index of the project column used in the independent-variable role. Because the interface resolves the column name and options from this index, reordering project columns later may associate an old result with a different column or make the table skip it.

Example

Description

List carrying dependent variable columns used in the result. Each item contains its column index; because axis and group names for categorical-to-categorical and categorical-to-numeric tables are produced from these mappings, the list must represent the same roles as the result JSON structure.

Example

Description

Zero-based index of the project column used in the dependent-variable role. Because the interface resolves the column name and options from this index, reordering project columns later may associate an old result with a different column or make the table skip it.

Example

Description

Zero-based index of the project column used in the before role. Because the interface resolves the column name and options from this index, reordering project columns later may associate an old result with a different column or make the table skip it.

Example

Description

Zero-based index of the project column used in the after role. Because the interface resolves the column name and options from this index, reordering project columns later may associate an old result with a different column or make the table skip it.

Example

Description

Stores the arithmetic mean of analyzed numeric data. When populated, it may be displayed in a metric card or result table; the value gains correct context only when interpreted with row count, analysis type, and related distribution measures.

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Description

Stores the median of analyzed numeric data. When populated, it may be displayed in a metric card or result table; the value gains correct context only when interpreted with row count, analysis type, and related distribution measures.

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Description

Stores the standard deviation of analyzed numeric data. When populated, it may be displayed in a metric card or result table; the value gains correct context only when interpreted with row count, analysis type, and related distribution measures.

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Description

Stores the Cronbach alpha internal-consistency coefficient of analyzed numeric data. When populated, it may be displayed in a metric card or result table; the value gains correct context only when interpreted with row count, analysis type, and related distribution measures.

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Description

Stores the minimum value of analyzed numeric data. When populated, it may be displayed in a metric card or result table; the value gains correct context only when interpreted with row count, analysis type, and related distribution measures.

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Description

Stores the maximum value of analyzed numeric data. When populated, it may be displayed in a metric card or result table; the value gains correct context only when interpreted with row count, analysis type, and related distribution measures.

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Description

Stores the sum of values of analyzed numeric data. When populated, it may be displayed in a metric card or result table; the value gains correct context only when interpreted with row count, analysis type, and related distribution measures.

Example

Description

Stores category-to-count mappings for a categorical column as a JSON object. The interface sums counts to calculate the valid-observation denominator and produces count, group percentage, and overall percentage for each category; malformed JSON prevents table generation.

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Description

Stores the nested category-to-count matrix of two categorical variables as a JSON object. The interface produces column-percentage, row-percentage, and raw-frequency tables from this structure; outer keys must represent row groups and inner keys must represent column categories.

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Description

Stores numeric summaries by categorical group as a JSON object. Each group may contain mean, median, interquartile range, first and third quartiles, standard deviation, minimum, maximum, sum, and count; cells appear empty when field names do not match the expected structure.

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Description

List carrying prebuilt result tables. The interface first attempts to generate tables from raw JSON fields 29 through 31; when none is produced, it uses parameter 33 name and parameter 34 JSON data from this list for backward compatibility.

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Description

Name of a table-list item. When it matches a translation key, the interface uses the localized title; otherwise it displays the stored text directly.

Example

Description

JSON data carrying columns and rows arrays for a table-list item. With a valid structure, the interface converts headers and rows into a table; malformed JSON or missing arrays prevents the table from being displayed.

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Description

Stores the interquartile range of the numeric distribution. In median display, the interface combines the first and third quartiles as “median (Q1–Q3)”; this summary is not displayed when a value is missing or cannot be converted to a number.

Example

Description

Stores the first quartile of the numeric distribution. In median display, the interface combines the first and third quartiles as “median (Q1–Q3)”; this summary is not displayed when a value is missing or cannot be converted to a number.

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Description

Stores the third quartile of the numeric distribution. In median display, the interface combines the first and third quartiles as “median (Q1–Q3)”; this summary is not displayed when a value is missing or cannot be converted to a number.

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Description

P value of the Levene test evaluating equality of group variances. The interface displays both fields together in test-selection assessment; when the p value is below the significance threshold, the equal-variance assumption is not supported.

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Description

Test statistic of the Levene test evaluating equality of group variances. The interface displays both fields together in test-selection assessment; when the p value is below the significance threshold, the equal-variance assumption is not supported.

Example

Description

JSON dictionary carrying the excess-kurtosis value and observation count for each group. The interface reports marked shape departure when absolute skewness exceeds 2 or absolute excess kurtosis exceeds 7; malformed JSON cannot produce diagnostic rows.

Example

Description

JSON dictionary carrying the skewness value and observation count for each group. The interface reports marked shape departure when absolute skewness exceeds 2 or absolute excess kurtosis exceeds 7; malformed JSON cannot produce diagnostic rows.

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Description

JSON dictionary carrying Shapiro–Wilk availability, test statistic, p value, and observation count for each group. For available groups, the interface assesses normality as rejected when the p value is below the significance threshold.

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Description

JSON structure carrying Kolmogorov–Smirnov distribution results by group. The interface displays each group’s test statistic, p value, and observation count from the groups object in the distribution-diagnostics table; without the expected groups structure, these test rows remain empty.

Example

Role of the definition

This layer stores the machine-learning analysis definition that runs on the prepared dataset of the shared Wanalyzer project. The definition determines model selection, input columns and their combination mode, output-column interpretation, the K-means cluster count, and automatic execution after data preparation. Because column relationships are stored as zero-based indexes, mappings must be verified again when column order changes. When automatic model and automatic combination are used together, the process can generate many results because every model is tried for every non-empty input subset.

0. status

Description

Stores the active state of the machine-learning definition. Value 1 stores the definition as active, while value 0 stores it as passive. However, the inspected automatic-analysis selector checks only parameter 14, and the manual execution flow does not contain a shared check that uses this field as a mandatory blocker. Therefore, do not treat passive as a security switch that certainly prevents execution; also set parameter 14 to no to disable automatic execution.

Options

Example

                      1 — aktif / active
                    

1. wanalyzer project id

Description

The client identifier that links the definition to the shared Wanalyzer project, column, and dataset context. When the correct identifier is used, the analysis reads the prepared dataset and column metadata of the same project; an incorrect identifier causes the definition to be searched under another project or prevents the required data and column relationships from being established.

Example

                      wp_01HXYZ…
                    

2. name

Description

The name used to distinguish the machine-learning definition in lists. When the quick-analysis screen creates a record, it automatically builds this name by combining the model usage mode, selected model, input columns, and output column; a descriptive name makes it easier to identify the configuration of each experiment when a project contains many experiments.

Example

                      Otomatik — Yaş, Gelir → Satın Alma
                    

3. note

Description

The explanation stored for the purpose, data scope, or evaluation notes of the definition. It is not used as an input variable during model training; therefore, use it to tell people why the experiment was created or under which conditions its results should be interpreted.

Example

                      Son 12 aylık kayıtlarla yeniden başvuru tahmini.
                    

4. model usage type

Description

Determines the set of models to execute. When selected is used, only the model in parameter 5 is trained. When automatic is used, the backend tries all seven model codes; consequently, the possible result count increases by the product of the model count and the input-combination count, and a model that is incompatible with the data may produce a result record containing a warning instead of a successful metric set.

Options

Example

                      1 — otomatik / auto
                    

5. model

Description

Determines the algorithm to train when model usage type is selected. Logistic regression and the current support-vector-machine path perform classification; linear regression performs regression; random forest, decision tree, and k-nearest neighbor become classifiers or regressors according to whether the output column is interpreted as categorical or numeric. K-means clusters the inputs without using the output column in training.

Options

Example

                      4 — rassal orman / random forest
                    

6. cluster count

Description

The requested number of clusters for the K-means model. If left empty, the backend uses 3; during training, the value is limited to at least 2 and at most the number of usable rows. For example, if 10 is requested but only 6 valid rows exist, the model runs with 6 clusters.

Example

                      3
                    

7. input usage type

Description

Determines how selected input columns are distributed across experiments. With all inputs together, every input is used as one feature set. With automatic combination, every non-empty subset from one column through all columns is generated; because n inputs produce 2ⁿ−1 combinations, execution time and result count can grow quickly. The quick-analysis interface sends this field only when the output is numeric; otherwise, the backend treats an empty value as all inputs together.

Options

Example

                      0 — tüm girdiler birlikte / all inputs together
                    

8. input column

Description

The repeating list of columns that the model uses as independent variables or features. At least one input is required. Categorical inputs are converted into indicator columns before training; numeric inputs are converted to numbers, and rows containing invalid numeric values are removed from the training data. The index and usage type in child records are stored separately for each input.

Example

                      [Yaş, Gelir, Şehir]
                    

9. input column index

Description

The zero-based position of the input-list row in the project column array. Value 0 refers to the first column and 1 to the second. If project columns are reordered, the same number points to another column, so the definition must be verified again.

Example

                      2
                    

10. input column usage type

Description

Determines whether each input column is interpreted as categorical or numeric. With automatic, float and integer types in project-column metadata are treated as numeric and all other types as categorical. With categorical, values are expanded into indicator columns; with numeric, values are converted to numbers and rows that cannot be converted are removed. The current quick-analysis record builder stores this field as automatic.

Options

Example

                      0 — otomatik / auto
                    

11. output column index

Description

The zero-based position of the dependent variable to predict in the project column array. This column is used as the target during classification and regression training. The K-means backend does not use a target during training; nevertheless, the current quick-analysis interface requires an output column before enabling execution.

Example

                      5
                    

12. output column usage type

Description

Determines whether the output is interpreted as a categorical or numeric target. With automatic, the project column type is used. A categorical output is converted into class labels; a numeric output is converted to numbers and enables regression models. The current quick-analysis record builder stores this field as automatic.

Options

Example

                      0 — otomatik / auto
                    

13. output column option target category

Description

The label to treat as the positive class for a categorical output with two options. When supplied, the backend converts rows whose output text exactly matches this label to 1 and all other rows to 0; therefore, the stored label must match the dataset value. The quick-analysis screen displays this field only for an output with exactly two options and stores the visible option label.

Example

                      Evet
                    

14. automatic analysis after project data preparation

Description

Determines whether an automatic machine-learning process is created for this definition after the project data-preparation process completes. With yes, the automatic-analysis scan selects the definition and creates a new process record in start status; with no, no automatic process is created after data preparation. Because the inspected selector does not additionally check the status field, the verified way to disable automatic execution is to set this field to no.

Options

Example

                      1 — evet / yes
                    

15. column filter rule json

Description

Stores, as JSON, the rule structure generated from column filters on the quick-analysis screen. The interface joins populated rules with an “and” relationship and writes column indexes and values into the record. However, although the inspected MLProcessAnalyzer version parses the field as JSON, it does not call a filtering operation that applies the rule to dataset rows; therefore, in the current backend behavior, populating this field does not guarantee that the rows entering training will change.

Example

                      {"query_list":[…],"compile_str_array":["(",0,")"]}
                    

Process flow

The process is created with start status, current start and last-information times, and zero counters. Start and continue are actively tracked; cancel and complete end tracking and refresh results.

Description

Lifecycle status of the machine-learning analysis process. The record is created with start for both manual and automatic triggers. When start or continue is selected, the interface treats the process as active and refreshes it every five seconds; when cancel or complete is selected, tracking stops and process results are reloaded.

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Client identifier connecting the process to its owning Wanalyzer project. With the correct identifier, the process is stored in the hierarchy of project, machine-learning definition, and process; an incorrect identifier may cause results to be searched under another project context and break the authorization relation.

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Client identifier of the machine-learning definition that produced the process. Because the process list reads child records under this identifier, the correct value makes the execution appear in the history of the related model configuration; an incorrect value associates it with a different definition.

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Start time at which the process is created. Both manual and automatic creation code fill this field in Coordinated Universal Time with second precision; if the end date is also recorded, the two values provide the approximate execution duration.

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Time at which machine-learning execution ends. When populated upon completion, it defines the execution interval together with the start date; it may remain empty while the process is running or when the executor ends without writing a date.

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Shows when the process most recently reported progress. It is initialized to the same value as the start date; when the executor updates it during progress, it becomes an activity indicator, while a value unchanged for a long time may indicate a delayed or stalled process.

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Stores the time at which the process was stopped or cancelled. When populated together with cancel status, it explains when execution was terminated; a value in this field alone does not end interface tracking because the tracking decision is based on status.

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Holds the number of dataset rows associated with or processed by the machine-learning execution. It is initialized to zero when the process is created and indicates training or analysis scope as the executor updates it. Zero alone does not mean failure because the process may have only just started.

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Holds the total number of machine-learning result records produced under the process. It is initialized to zero; when the executor updates it as results are written to the child layer, it indicates expected result volume. Detailed model, metric, and interpretation data reside in the result child layer, not in this counter.

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Text carrying the trigger mode, progress notes, and error descriptions of the process. Manual triggering records authorization or credit execution mode, while automatic triggering records the post-project-data-preparation automatic-analysis note; the log therefore distinguishes why and how the process was started.

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Result flow

Model type determines interpretation of related classification, regression, or clustering measures. Cross-validation describes generalization stability, while calibration describes probability reliability.

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Status field of the result record. Because no status options were supplied and the inspected result view does not filter by this field, no activation behavior has been assumed for particular values; the field carries status information written by the result producer.

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Client identifier connecting the result to the related Wanalyzer project. With the correct identifier, the result is found in the project → machine learning → process → result hierarchy; an incorrect identifier may direct listing, authorization, or trained-model lookup to a different context.

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Client identifier connecting the result to the related machine-learning definition. With the correct identifier, the result is found in the project → machine learning → process → result hierarchy; an incorrect identifier may direct listing, authorization, or trained-model lookup to a different context.

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Client identifier connecting the result to the related machine-learning process. With the correct identifier, the result is found in the project → machine learning → process → result hierarchy; an incorrect identifier may direct listing, authorization, or trained-model lookup to a different context.

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Time when model-result generation started. Together with the end date, it supports approximate calculation of training and evaluation duration.

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Time when model-result generation finished. Together with the start date, it defines the execution interval; if empty, generation may be unfinished or the date may not have been written.

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Model family that produced the result. This selection determines the interpretation context for classification, regression, or clustering metrics; for example, accuracy applies to classification, R² and error measures to regression, and silhouette, Calinski–Harabasz, and Davies–Bouldin measures to clustering.

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List carrying project columns used as model inputs. Each item contains parameter 8, a zero-based column index; when column order changes, mappings must be checked again because a training variable may resolve to the wrong column name.

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Zero-based index of the project column used in the model’s input role. The interface resolves the column name from this index in the history title; after reordering, an old result may be displayed with a different column name.

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Zero-based index of the project column used in the model’s output role. The interface resolves the column name from this index in the history title; after reordering, an old result may be displayed with a different column name. Unsupervised clustering models may not have a defined output column.

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Serialized pickle data of the trained model object. Prediction code attempts to decode this field first as Base64, then as a byte literal, and finally as Latin-1-compatible text before loading it with pickle; when loading fails, no trained model is found. Pickle can execute code from an untrusted source, so only trusted system-generated data must be stored.

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Ratio of data reserved for evaluation to the complete dataset. A larger ratio bases test metrics on more observations but leaves less data for training; a smaller ratio trains the model on more data but may increase evaluation uncertainty.

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Proportion of correct predictions among all predictions in classification. With imbalanced classes, high accuracy may hide weak minority-class performance, so it must be interpreted with precision, recall, F-score, and the confusion matrix.

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Area under the receiver operating characteristic curve summarizing the classification model’s ability to separate classes across thresholds. A higher value indicates stronger overall discrimination, while class balance, calibration, and the operating threshold still require separate evaluation.

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precision value calculated for the positive class of binary classification. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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precision value calculated for decisions from all classes through global totals. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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precision value calculated for each class with equal weight. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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precision value calculated for each class weighted by its observation count. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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recall value calculated for the positive class of binary classification. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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recall value calculated for decisions from all classes through global totals. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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recall value calculated for each class with equal weight. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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recall value calculated for each class weighted by its observation count. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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F-score value calculated for the positive class of binary classification. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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F-score value calculated for decisions from all classes through global totals. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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F-score value calculated for each class with equal weight. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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F-score value calculated for each class weighted by its observation count. A larger value indicates better performance under that evaluation mode; because the averaging method changes the result according to class balance, it must not be confused with the other variants.

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R² score showing how much variance in the target variable is explained by the regression model. Fit improves as the value approaches 1; a value near zero indicates no improvement over predicting the mean, while a negative value may indicate worse performance.

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Name of the splitting method used for cross-validation. A method appropriate to the data structure provides a more reliable estimate of generalization; ignoring time, group, or class structure may cause data leakage.

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Number of folds into which data is divided for cross-validation. More folds create more training-validation repetitions and increase execution cost; the value must not exceed the number of samples.

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Text carrying accuracy values calculated for every cross-validation fold. Individual scores expose variation between folds in addition to the mean; the number of values should agree with the fold count.

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Cross-validation mean accuracy obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation accuracy standard deviation obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation maximum accuracy obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation minimum accuracy obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation mean roc auc obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation roc auc standard deviation obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation mean r2 obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation r2 standard deviation obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation maximum r2 obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Cross-validation minimum r2 obtained from cross-validation repetitions. Unlike a single test split, it summarizes generalization behavior across folds; the mean describes performance, standard deviation describes stability, and minimum and maximum identify the worst and best folds.

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Brier score measuring squared distance between predicted probabilities and observed outcomes. A lower value indicates better probability calibration; it should be evaluated together with class prevalence and model discrimination.

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Readable calibration class derived from the Brier score and analysis rules. Good, moderate, or poor summarizes probability reliability; without the numeric Brier score it should not be used alone for definitive model comparison.

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silhouette score of the clustering result. In general, a higher value indicates that clusters are compact internally and separated from one another; comparisons across data scales and cluster counts must use the same preparation conditions.

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Calinski–Harabasz score of the clustering result. In general, a higher value indicates that between-cluster separation is strong relative to within-cluster dispersion; comparisons across data scales and cluster counts must use the same preparation conditions.

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Davies–Bouldin score of the clustering result. In general, a lower value indicates that clusters are better separated; comparisons across data scales and cluster counts must use the same preparation conditions.

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mean absolute error of regression predictions. A lower value indicates better fit and expresses absolute error magnitude in the original target unit; models with different target scales must not be compared directly.

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mean squared error of regression predictions. A lower value indicates better fit and expresses large errors more strongly by squaring them; models with different target scales must not be compared directly.

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root mean squared error of regression predictions. A lower value indicates better fit and expresses large errors strongly while returning the result to the target unit; models with different target scales must not be compared directly.

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Text carrying contributions of input variables to model prediction. Because values may be model-specific coefficients or importance measures, magnitude and sign must be interpreted with the related model method and must not be treated as evidence of causality.

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Warning describing a data, model, or metric limitation encountered during training or evaluation. When populated, the interface displays a prominent warning block; its effect must be evaluated before the result is used.

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Confusion-matrix JSON data carrying actual and predicted class counts for classification. The interface processes a matrix or values array with optional labels, columns, and row_labels; without labels it generates zero-based positions, and with an invalid matrix it creates no view.

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List of table definitions displayed with the result. Each item carries parameter 52 title and parameter 53 JSON data; when valid columns and rows exist, the interface displays the table and allows it to be added to article content.

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User-visible title of a result table. When empty, the interface creates a fallback title from the table parameter name and sequence number; a descriptive title distinguishes different tables.

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JSON value carrying table data structure. The interface accepts an object with columns and rows, an array of objects, or a two-dimensional array whose first row is the header; when none of these forms is provided, the table is not displayed.

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List carrying readable interpretations of the same result in different languages. Each item stores parameter 55 content with parameter 56 language; the interface selects text matching the current language and displays the first item when no match exists.

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Readable interpretation content in a result-text item. When it explains model performance, limitations, and usage conditions consistently with the metrics, the user can evaluate raw values in the correct context.

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Turkish or English language code of a result-text item. With the correct code, the interface displays content matching the user’s language; an incorrect code makes the text appear under another language option.

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