Implement Linear Regression
78.4% acceptance
EasySupervised Learning
Commonly asked at:
GoogleWhy this matters:
Tests understanding of gradient descent optimization – a foundational concept asked in 80% of ML interviews.
Given a dataset of features X and targets y, implement the fit() and predict() methods for a LinearRegression class.
Your implementation should minimize the Mean Squared Error (MSE) loss using gradient descent.
Examples
Input:
X = [[1], [2], [3]], y = [2, 4, 6]
Output:
predictions ≈ [2, 4, 6]
Explanation:
The model should learn the relationship y = 2x
Constraints
- 1 <= len(X) <= 10000
- Learning rate: alpha = 0.01
- Iterations: 1000
Tags
model-evaluationrecsys-rankingregularizationregression