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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Evaluation Results

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