metadata
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: skops-b2ie2xry.pkl
widget:
- structuredData:
Age:
- -0.7989508220667412
- -0.021264850777441783
- -0.3128970900109291
EstimatedSalary:
- 0.4946075830589406
- -0.5773590622674106
- 0.14694272511525913
example_title: social-network-ads
datasets:
- saifhmb/social-network-ads
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