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Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter Value
memory
steps [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('classifier', RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=3,
min_samples_split=3, n_estimators=103,
random_state=404159593))]
verbose False
imputer SimpleImputer(strategy='median')
scaler StandardScaler()
classifier RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=3,
min_samples_split=3, n_estimators=103,
random_state=404159593)
imputer__add_indicator False
imputer__copy True
imputer__fill_value
imputer__keep_empty_features False
imputer__missing_values nan
imputer__strategy median
scaler__copy True
scaler__with_mean True
scaler__with_std True
classifier__bootstrap True
classifier__ccp_alpha 0.0
classifier__class_weight balanced_subsample
classifier__criterion gini
classifier__max_depth
classifier__max_features sqrt
classifier__max_leaf_nodes
classifier__max_samples
classifier__min_impurity_decrease 0.0
classifier__min_samples_leaf 3
classifier__min_samples_split 3
classifier__min_weight_fraction_leaf 0.0
classifier__monotonic_cst
classifier__n_estimators 103
classifier__n_jobs
classifier__oob_score False
classifier__random_state 404159593
classifier__verbose 0
classifier__warm_start False

Model Plot

Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),('scaler', StandardScaler()),('classifier',RandomForestClassifier(class_weight='balanced_subsample',min_samples_leaf=3, min_samples_split=3,n_estimators=103,random_state=404159593))])
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