The data analysis tool automates the analysis of processed training data and its relation to the target variable. A report is generated during model training for each solution. Given the potentially wide feature space, up to 20 of the most important features with the highest statistical significance are selected for analysis based on machine learning modeling.
To access the tool, go to the :material-cursor-default-click: Model Training tab and click the Data Analysis button.
Note
The final model might indicate a slightly different set of features with the highest importance due to the comprehensive number of iterations during training. This can result in discrepancies between the features selected for data analysis and the final Feature Importance Matrix.
The data analysis tool contains the following sections:
| Section | Description |
|---|---|
| Dataset overview | Displays brief statistics of your training dataset, including problem type, dataset dimensions, missing values, and record count. |
| Continuous data distribution and relation to the target variable | Visualizes each continuous variable using two plots: Variable density distribution chart — A density plot that visualizes the distribution of data across all rows in the dataset. This chart is a variation of a histogram that uses kernel smoothing to plot values, producing smoother distributions by reducing noise. The peaks of the density plot show where values are concentrated over the interval. Feature relation to the target variable — This chart is presented in one of two formats: • A line chart showing how the continuous variable changes with changes in the continuous target variable (regression task type). • A histogram showing the mean continuous variable value for each class of the target variable (classification task type). |
| Feature correlations | Visualizes correlations in the data using two plots: Heatmap — Displays the binary correlation of the 10 most important variables between each other and with the target variable. The 10 most important features are selected based on the binary correlation of the features with the target variable. Histogram (horizontal) — Displays the level of high mutual correlation between independent variable pairs. Pairs are selected if the value of their mutual correlation exceeds 0.7. |
| Target variable distribution | Visualizes the target variable statistics in one of two formats: Violin plot — Displays the distribution, median, and outliers in the target variable (regression task type). Histogram/count plot — Displays the number and percentage of each target class throughout the entire dataset (classification task type). |
| Outliers | Visualizes outliers through a scatter plot that illustrates how individual data points are distributed relative to the target variable (regression task type). |