Model description

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Intended uses & limitations

This model is not ready to be used in production (J).

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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Hyperparameter	Value
memory
steps	[('imputer', SimpleImputer()), ('scaler', StandardScaler()), ('model', LogisticRegression())]
verbose	False
imputer	SimpleImputer()
scaler	StandardScaler()
model	LogisticRegression()
imputer__add_indicator	False
imputer__copy	True
imputer__fill_value
imputer__keep_empty_features	False
imputer__missing_values	nan
imputer__strategy	mean
imputer__verbose	deprecated
scaler__copy	True
scaler__with_mean	True
scaler__with_std	True
model__C	1.0
model__class_weight
model__dual	False
model__fit_intercept	True
model__intercept_scaling	1
model__l1_ratio
model__max_iter	100
model__multi_class	auto
model__n_jobs
model__penalty	l2
model__random_state
model__solver	lbfgs
model__tol	0.0001
model__verbose	0
model__warm_start	False

Model Plot

The model plot is below.

Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),('model', LogisticRegression())])

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Evaluation Results

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How to Get Started with the Model

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Model Card Authors

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Citation

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