Choosing the Right Parameters for Logistic Regression: A Step-by-Step Guide to Optimal Performance
This comprehensive guide explains how to select the right parameters for logistic regression. Learn about solvers, regularization techniques, cross-validation, and feature selection methods like RFE, wrapper approach, and filter methods. Improve your model’s performance with these essential tips.
Choosing the right parameters for logistic regression is essential to achieve optimal performance of the model. Here are some steps we can follow to choose the right parameters:
- Choose a solver: Logistic regression is solved using numerical optimization techniques. The choice of solver can impact the model’s performance. Some commonly used solvers are ‘lbfgs’, ‘newton-cg’, and ‘liblinear’. liblinear is used by most.
- Choose a regularization technique: Regularization is used to avoid overfitting in logistic regression. L1 and L2 regularization are commonly used techniques. L1 (Ridge) regularization performs feature selection, while L2 (LASSO) regularization shrinks the coefficients of the features.
- Choose the…
