stummala521/AI4SAR

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	[('columntransformer', ColumnTransformer(transformers=[('standardscaler', StandardScaler(), ['location_found_elevation']), ('onehotencoder', OneHotEncoder(), ['situation'])])), ('randomforestclassifier', RandomForestClassifier(class_weight={False: 1.9217032967032968, True: 0.6758454106280193}, random_state=42))]
verbose	False
columntransformer	ColumnTransformer(transformers=[('standardscaler', StandardScaler(), ['location_found_elevation']), ('onehotencoder', OneHotEncoder(), ['situation'])])
randomforestclassifier	RandomForestClassifier(class_weight={False: 1.9217032967032968, True: 0.6758454106280193}, random_state=42)
columntransformer__n_jobs
columntransformer__remainder	drop
columntransformer__sparse_threshold	0.3
columntransformer__transformer_weights
columntransformer__transformers	[('standardscaler', StandardScaler(), ['location_found_elevation']), ('onehotencoder', OneHotEncoder(), ['situation'])]
columntransformer__verbose	False
columntransformer__verbose_feature_names_out	True
columntransformer__standardscaler	StandardScaler()
columntransformer__onehotencoder	OneHotEncoder()
columntransformer__standardscaler__copy	True
columntransformer__standardscaler__with_mean	True
columntransformer__standardscaler__with_std	True
columntransformer__onehotencoder__categories	auto
columntransformer__onehotencoder__drop
columntransformer__onehotencoder__dtype	<class 'numpy.float64'>
columntransformer__onehotencoder__feature_name_combiner	concat
columntransformer__onehotencoder__handle_unknown	error
columntransformer__onehotencoder__max_categories
columntransformer__onehotencoder__min_frequency
columntransformer__onehotencoder__sparse	deprecated
columntransformer__onehotencoder__sparse_output	True
randomforestclassifier__bootstrap	True
randomforestclassifier__ccp_alpha	0.0
randomforestclassifier__class_weight	{False: 1.9217032967032968, True: 0.6758454106280193}
randomforestclassifier__criterion	gini
randomforestclassifier__max_depth
randomforestclassifier__max_features	sqrt
randomforestclassifier__max_leaf_nodes
randomforestclassifier__max_samples
randomforestclassifier__min_impurity_decrease	0.0
randomforestclassifier__min_samples_leaf	1
randomforestclassifier__min_samples_split	2
randomforestclassifier__min_weight_fraction_leaf	0.0
randomforestclassifier__n_estimators	100
randomforestclassifier__n_jobs
randomforestclassifier__oob_score	False
randomforestclassifier__random_state	42
randomforestclassifier__verbose	0
randomforestclassifier__warm_start	False

Model Plot

Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('standardscaler',StandardScaler(),['location_found_elevation']),('onehotencoder',OneHotEncoder(),['situation'])])),('randomforestclassifier',RandomForestClassifier(class_weight={False: 1.9217032967032968,True: 0.6758454106280193},random_state=42))])

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Evaluation Results

Metric	Value
accuracy	0.698333
f1_score	0.698018

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model_description

RandomForestClassifier model for tabular classification.

eval_method

Evaluated using test split.

confusion_matrix