The categorical features table summarizes multiple statistics about every categorical feature in the data, allowing it to be investigated all at once. The statistics presented in the table include: Uniques - Number of unique categories in each categorical feature in the data-set. N/A - Number of missing values in each categorical feature in the data-set. Least frequent- the least frequent category and it's appearance frequency in percentage for each categorical feature in the

Features often interact with each other under model predictions, and therefore interaction analysis can provide meaningful insights for this model. The predictions of a model can be decomposed into the sum of feature effects and the feature interaction effects. Here, Interactions are defined as the effect on the models' predictions that occurs by varying the features values after considering their individual contributions. For example, a house pricing model can use individual

The numerical features table summarizes multiple statistics about every numerical feature in the data, allowing it to be investigated all at once. The statistics presented in the table include: Mean - the mean value of each numerical feature in the data-set Stdev - the standard deviation of each numerical feature in the data-set Min - the minimal value of each numerical feature in the data-set Max - the maximal value of each numerical feature in the data-set N/A - Number of m

Shared distribution heatmap is a very useful and visual way to examine the spread and legitimacy of the data and understand how different attributes appear together in the data. For each pair of features we count the amount of samples that have every combination of their values. For categorical features this is done per each category. For continuous features, the values are first binned and the count is done per each bin. The x-axis and y-axis of the heatmap represent the cho