Comments (3)
Is it helpful?
from docs.
Available modes | |||
---|---|---|---|
explain | perform | compete | |
Uses models: | |||
Baseline | + | ||
Linear | + | + | + |
Decision Tree | + | + | |
Random Forest | + | + | + |
XGBoost | + | + | + |
Neural Network | + | + | + |
Ensemble | + | + | + |
Light GBM | + | ||
CatBoost | + | + | |
Extra Trees | + | ||
Nearest Neighbors | + | ||
Stacking | + | ||
Validation: | |||
Uses 75%/25% train/test split | Uses a 5-fold CV (Cross-Validation) with shuffle and stratification | Uses 10-fold CV (Cross-Validation) with shuffle and stratification | |
Explanations: | |||
explain_level=2 | explain_level=1 | explain_level=0 | |
Steps: | |||
Learning curves | + | + | + |
Importance plots | + | + | |
SHAP plots | + | ||
Steps | |||
simple_algorithms | + | + | + |
default_algorithms | + | + | + |
not_so_random | + | + | |
golden_features | + | + | |
insert_random_feature | + | + | |
feature_selection | + | + | |
hill_climbing_1 | + | + | |
hill_climbing_2 | + | + | |
ensemble | + | + | + |
stack | + | ||
ensemble_stacked | + |
from docs.
Thank you @aplonska ! That's what I need!
from docs.
Related Issues (4)
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from docs.