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
Click to expand
| 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.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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
- Downloads last month
- 0
This model does not have enough activity to be deployed to Inference API (serverless) yet.
Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.