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Model description

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Intended uses & limitations

This model is not ready to be used in production.

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__missing_values nan
imputer__strategy mean
imputer__verbose 0
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())])
Please rerun this cell to show the HTML repr or trust the notebook.

Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric Value
accuracy 0.982456
f1 score 0.982456

How to Get Started with the Model

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Model Card Authors

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