Model description
This is a logistic regression classifier trained on social network ads dataset (https://huggingface.co/datasets/saifhmb/social-network-ads).
Training Procedure
The preprocesing steps include using a train/test split ratio of 80/20 and applying feature scaling on all the features.
Hyperparameters
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| Hyperparameter | Value |
|---|---|
| C | 1.0 |
| class_weight | |
| dual | False |
| fit_intercept | True |
| intercept_scaling | 1 |
| l1_ratio | |
| max_iter | 100 |
| multi_class | auto |
| n_jobs | |
| penalty | l2 |
| random_state | |
| solver | lbfgs |
| tol | 0.0001 |
| verbose | 0 |
| warm_start | False |
Model Plot
LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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LogisticRegression()
Evaluation Results
| Metric | Value |
|---|---|
| accuracy | 0.925 |
| precision | 0.944444 |
| recall | 0.772727 |
Model Explainability
SHAP was used to determine the important features that helps the model make decisions
Confusion Matrix
Model Card Authors
This model card is written by following authors: Seifullah Bello
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