YAML Metadata
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Model description
[More Information Needed]
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.
Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),('model', LogisticRegression())])
SimpleImputer()
StandardScaler()
LogisticRegression()
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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Citation
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BibTeX:
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