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 regularization parameter: The regularization parameter controls the strength of the regularization. It is denoted by ‘c’ in scikit-learn. A smaller value of cincreases the strength of the regularization, while a larger value of c reduces it.
- Cross-validation: Once we have chosen the best parameters, we should perform cross-validation to ensure that the model’s performance is consistent across different folds.
Let’s check a sample python code to understand the value of C- regularization parameter. I will use breast cancer dataset from sklearn.
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
# Load the breast cancer dataset
data = load_breast_cancer()
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)