Exploratory data analysis (EDA) component - ClickML Studio

The EDA component runs statistical analyses on your DataFrame and returns charts and summaries you can explore in the canvas. Use it to understand your data’s structure, spot problems, and decide what preprocessing to apply.

Configuration

Option	Description
Analysis Type	The analysis to run (see below).
Features	Columns to include in the analysis (used by Distribution and Outlier Detection).

Analysis types

Type	What you get
Correlation Matrix	Heatmap of Pearson correlations between all numerical features. Shows the top 10 most correlated pairs.
Missing Values	Bar chart of missing value counts and percentages per column. Flags completely missing columns.
Statistics	Descriptive statistics table: count, mean, std, min, quartiles, max, skewness, kurtosis for numerical columns; top value, frequency, and unique count for categorical columns.
Distribution	Histogram with summary stats (mean, median, std, skewness) for each selected feature.
Outlier Detection	Box-plot style outlier summary using the IQR method (1.5× IQR rule). Shows outlier count and percentage per feature.

Input / Output

	Type
Input	DataFrame
Output	Analytics data (displayed in the canvas — not passed downstream)

Run EDA right after loading your data to get a quick overview before deciding which preprocessing components to add.