This Jupyter Notebook demonstrates hyperparameter tuning for a Logistic Regression model using Python, with a focus on regularization techniques (L1 and L2). It explains how tuning parameters impacts model performance and helps prevent overfitting in classification tasks.
-
Notifications
You must be signed in to change notification settings - Fork 0
This Jupyter Notebook demonstrates hyperparameter tuning for a Logistic Regression model using Python, with a focus on regularization techniques (L1 and L2). It explains how tuning parameters impacts model performance and helps prevent overfitting in classification tasks.
AmritaPanjwani/Logistic_Regression_Hyperparameter_Tuning
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
This Jupyter Notebook demonstrates hyperparameter tuning for a Logistic Regression model using Python, with a focus on regularization techniques (L1 and L2). It explains how tuning parameters impacts model performance and helps prevent overfitting in classification tasks.
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published