Inspecting Learned Rules

Because nothing in lochan-eda is a black box, every rule learned during an is_train=True pass is stored as a plain attribute on the handler, so you can audit why a decision was made.

eda = AutomatedEDA()
eda.run_pipeline(df=X_train, target=y_train, is_train=True)

# Which columns were dropped, and why?
print(eda.num_handler.drop_cols_)      # numeric columns dropped (>40% missing)
print(eda.cat_handler.drop_cols_)      # categorical columns dropped (>40% missing)

# What imputation value did each column get?
print(eda.num_handler.impute_values_)  # {column: fill_value}

# What outlier rule and scaler did each numeric column get?
print(eda.num_handler.outlier_rules_)  # {column: {"type": "clip"/"sqrt"/"log1p", ...}}
print(eda.num_handler.scalers_)        # {column: fitted sklearn scaler object}

# What encoding strategy did each categorical column get?
print(eda.cat_handler.encode_types_)   # {column: "binary"/"ohe"/"te"/"freq"}

# The exact column order the final model-ready output must match
print(eda.final_columns_)

Full attribute reference

Attribute	Owner	Description
drop_cols_	HandleNumerical, HandleCategorical	Columns dropped for exceeding the 40% missing-value threshold.
impute_values_	HandleNumerical	{column: fill_value} used for imputation.
outlier_rules_	HandleNumerical	{column: rule_dict} describing the outlier treatment applied.
scalers_	HandleNumerical	{column: fitted scaler}.
transforms_	HandleNumerical	{column: "log1p"} for pre-scaling log transforms.
impute_modes_	HandleCategorical	{column: mode_value} used for imputation.
rare_cats_	HandleCategorical	{column: [rare category values]} grouped into "Other".
encode_types_	HandleCategorical	{column: encoding strategy}.
binary_maps_	HandleCategorical	{column: {value: 0/1}}.
ohe_columns_	HandleCategorical	{column: [one-hot column names]}.
freq_maps_	HandleCategorical	{column: {value: frequency}}.
target_encoders_	HandleCategorical	{column: fitted sklearn TargetEncoder}.
final_columns_	AutomatedEDA	Full training-time output column order, used to align test-time output.

This is especially useful for debugging a mismatch between expected and actual output columns, or for explaining preprocessing decisions in a report or model card.