merve/my-awesome-model-blog

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

This model card is written by following authors:

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Model Card Contact

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Citation

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BibTeX:

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