Train Logistic Regression with Gradient Descent
Linear Models DS practice problem on Onlearn.
Difficulty: hard.
Topics: Understanding Train Logistic Regression with Gradient Descent, Binary Cross Entropy, Sigmoid Activation, Learning Rate, Partial Derivatives, Parameter Initialization, Optimization Theory, Statistical Learning, Linear Algebra, Numerical Analysis, Calculus, Gradient Descent, Supervised Classification, Loss Function Formulation, Vectorized Operations, Iterative Convergence.
Implement a gradient descent based training algorithm for logistic regression. Your task is to compute model parameters using Binary Cross Entropy loss and return the optimized coefficients along with collected loss values over iterations(round to the 4th decimal).