Top 20 Logistic Regression Interview Questions and Answers with Examples

AI & Education Hub 3:05 PM
Top 20 Logistic Regression Interview Questions - Responsive

Mastering Logistic Regression: Top 20 Interview Questions & Answers

🚀 Conquer your Machine Learning interviews! This guide covers the most frequently asked questions on Logistic Regression with clear, mobile-friendly explanations and inline SVG diagrams that work without external images.

Index & Navigation

Section 1: Introduction to Logistic Regression

This section covers foundational questions often asked to gauge your basic understanding.

Q. No.QuestionAnswer
1 What is Logistic Regression and why is it called 'Regression' if it's a classification model? Logistic Regression is a classification algorithm used to predict a binary outcome (e.g., 0/1). It's called 'Regression' because it models the linear relationship between features and the log-odds of the outcome; the linear output is converted to a probability via the sigmoid function.
2 Which algorithm is suitable for binary classification problems? Logistic Regression is widely used for binary classification due to simplicity and interpretability. Others include SVMs, Decision Trees, and KNN.
3 Which algorithm is NOT suitable for binary classification? Algorithms designed for continuous outputs (e.g., Linear Regression) are not directly suitable for classification unless adapted.

Section 2: Core Concepts & Functions

Lesson: Sigmoid & Probability

Q. No.QuestionAnswer
4 What is the Sigmoid Activation Function? The sigmoid maps any real number to (0,1), turning a linear score into a probability. 
σ(z) = 1 / (1 + e^(−z))
where z = w₀ + w₁x₁ + … + wₙxₙ
5 Explain the S-curve. The S-curve is the shape of the sigmoid. It transitions smoothly from 0 to 1 and is steepest near z=0.
Figure: S-curve (probability vs. linear input z).
6 What is a threshold? A cutoff (commonly 0.5) applied to predicted probabilities to convert them into class labels. Adjust to favour precision vs recall.
7 What are Odds and Log-Odds? Odds = P/(1−P). Log-odds (logit) = ln(odds). Logistic Regression models log-odds as a linear function of features.

Section 3: Mathematical Derivations & Underpinnings

Q. No.QuestionAnswer
8 How do we derive Logistic Regression from Linear Regression concepts? Start with a linear score z = w₀ + w₁x₁ + … . Model log-odds = z and invert using σ(z) to obtain probabilities.
9 What is the cost function? Why not MSE? Cross-Entropy (Log Loss) is used: 
L(ŷ,y) = −[ y·ln(ŷ) + (1−y)·ln(1−ŷ) ]
MSE with sigmoid can create a non-convex landscape; cross-entropy is better for optimization.
Convex (left) vs Non-convex (right) cost landscapes.
10 How is Gradient Descent applied? Update weights w ← w − η ∇L(w) iteratively; η is the learning rate.
11 Explain MLE in this context. MLE chooses parameters that maximize data likelihood; minimizing negative log-likelihood equals minimizing cross-entropy.

Section 4: Applications & Limitations

Q. No.QuestionAnswer
12 List common applications. Spam detection, disease prediction, credit scoring, churn prediction, sentiment analysis.
13 What are the assumptions?
  1. Binary outcome
  2. Independent observations
  3. No strong multicollinearity
  4. Linearity of log-odds
  5. Sufficient sample size
14 Limitations? Linear decision boundaries, sensitive to outliers, needs scaling for gradient methods, may underperform on complex non-linear data.
15 Multi-class classification? Use One-vs-Rest or Softmax (multinomial logistic regression).

Section 5: Advanced Topics & Model Evaluation

Q. No.QuestionAnswer
16 Interpreting coefficients? Coefficients act on log-odds. exp(β) is the odds ratio for a unit increase in the feature.
17 Evaluation metrics? Accuracy, Precision, Recall, F1, ROC, AUC, Confusion Matrix.
Confusion Matrix (TP/TN/FP/FN).
18 Regularization? L1 (Lasso) and L2 (Ridge) penalize large weights and reduce overfitting.
19 Handling imbalanced datasets? Resampling (SMOTE/undersample), class weights, threshold tuning, use recall/F1/AUC instead of accuracy.
20 When choose Logistic Regression? When you need a simple, interpretable model that outputs probabilities and has an approx linear boundary.

🎯Congratulations on completing this topic. All diagrams are inline SVG and responsive.Good Luck!

Tags

Post a Comment

0 Comments

I’m here to help! If you have any questions, just ask!