Binary Classification with Logistic Regression
Linear Models DS practice problem on Onlearn.
Difficulty: easy.
Topics: Understanding Binary Classification with Logistic Regression, Sigmoid Activation, Log-Odds Transformation, Decision Boundary, Binary Cross-Entropy, Classification Thresholding, Linear Algebra, Probability Theory, Optimization Theory, Statistical Learning, Information Theory, Vector Calculus, Maximum Likelihood Estimation, Gradient-based Optimization, Supervised Learning, Decision Theory.
Implement the prediction function for binary classification using Logistic Regression. Your task is to compute class probabilities using the sigmoid function and return binary predictions based on a threshold of 0.5.