⚖️ Logistic Regression in Sklearn: Handling Class Imbalance and Regularization

-

 Introduction

Logistic Regression is one of the most widely used algorithms for binary classification. While it is simple, powerful, and interpretable, two important aspects play a huge role in its performance:

  1. Class imbalance – when one class has far more samples than the other.

  2. Regularization – controlling model complexity to avoid overfitting.

In this blog, we’ll understand how class_weight='balanced' and the parameter C work in LogisticRegression from scikit-learn.

🔹 The Example Code

from sklearn.linear_model import LogisticRegression

model = LogisticRegression(class_weight='balanced', C=0.5)
model.fit(X, y)

This code trains a Logistic Regression model with:

  • class_weight='balanced'

  • C=0.5

Now let’s break down what this means.

🔹 Class Weight and Imbalanced Datasets

When classes are imbalanced (e.g., 90% negatives, 10% positives), the model might be biased towards the majority class.

👉 Setting class_weight='balanced':

  • Automatically adjusts weights inversely proportional to class frequencies.

  • Rare classes get higher weight so the model pays more attention to them.

✅ Correct understanding:

“The balanced mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data.”

❌ Wrong assumption:

"Equal weights are given to all classes." → This is incorrect, because weights are proportional to imbalance.

🔹 Regularization with Parameter C

Logistic Regression in sklearn always applies regularization unless penalty='none' is explicitly set.

In the given code:

✅ Correct understanding:

“The value of C indicates that the model will apply a regularization.”

❌ Wrong assumption:

“No regularization is applied because C is set.” → Incorrect.

🔹 Final Takeaways

  • class_weight='balanced' handles imbalanced datasets by adjusting weights inversely to class frequencies.

  • The C parameter controls regularization strength (smaller C = stronger regularization).

  • Logistic Regression in sklearn by default always applies regularization unless explicitly disabled.

👉 Pro Tip: Always check class balance in your dataset. If classes are highly skewed, use class_weight='balanced' or manually specify class weights to prevent bias.

Would you like me to also create a comparison table:
📊 “Logistic Regression with Default vs Balanced Class Weights” for better visualization in the blog?

Comments

Post a Comment

Popular posts from this blog

Understanding Data Leakage in Machine Learning: Causes, Examples, and Prevention

-
When building machine learning models, achieving high accuracy on your training or validation data is exciting — but sometimes, that success can be misleading. One common trap that causes overly optimistic results is data leakage . In this blog, we will explore: What is data leakage? Why is it harmful? Real-world examples to understand leakage better How to prevent data leakage in your projects What is Data Leakage? Data leakage happens when your machine learning model has access to information during training that it wouldn’t realistically have when making predictions in the real world. This extra information “leaks” from the future or from the test set, causing the model to cheat. When leakage occurs, your model's performance metrics (like accuracy, R², or F1-score ) look great, but this performance won’t generalize to new, unseen data. Why is Data Leakage a Problem? Overfitting to training data : The model learns shortcuts rather than meaningful pattern...
Read more

🌳 Understanding Maximum Leaf Nodes in Decision Trees (Scikit-Learn)

-
When working with decision trees in machine learning , one of the most common questions is: 👉 How many leaf nodes can a decision tree have, given certain constraints? Let’s walk through a real example. 📌 The Question We initialize a decision tree as follows: from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier( max_depth =4, random_state =42) We’re told: The dataset has 100 samples and 10 features . The tree is a perfectly balanced binary tree . No pruning is applied. No additional stopping criteria (like min_samples_split , min_samples_leaf , or max_leaf_nodes ). 👉 What is the maximum possible number of leaf nodes in the decision tree? 🌲 Key Concepts 1. Depth vs. Levels A binary tree with max_depth = d has at most d splits from the root to a leaf. Depth d = 4 means there can be 4 splits from the root node down to a leaf node. 2. Maximum Leaf Nodes Formula In a perfectly balanced binary tree : Number of leaf n...
Read more

Linear Regression with and without Intercept: Explained Simply

-
If you’re new to machine learning, linear regression is one of the easiest models to understand. It tries to draw a straight line (or a flat plane, if there are more features) that best fits the data. When we use scikit-learn’s LinearRegression , we can decide whether the line should include an intercept (a constant number added at the end of the equation) or not. The Example Code import numpy as np from sklearn.linear_model import LinearRegression X = np.array([[12, 13], [23, 24], [24, 25], [35, 36], [36, 37], [37, 38]]) # Create target values y y = np.dot(X, np.array([4, 5])) + 6 reg1 = LinearRegression(fit_intercept=True).fit(X, y) s1 = reg1.score(X, y) reg2 = LinearRegression(fit_intercept=False).fit(X, y) s2 = reg2.score(X, y) What’s Happening Here? X is our input data (two features per row). y is the output we want to predict. We made it using this formula: y = 4 ∗ x 0 + 5 ∗ x 1 + 6 y = 4 * x_0 + 5 * x_1 + 6 This formula clearly has an intercept = 6 . Case 1: Wi...
Read more