Logistic Regression

medium· Machine Learningruns: 0

Implement logistic regression for binary classification, trained with batch gradient descent on the binary cross-entropy loss. The model fits weights and bias such that sigmoid(X @ w + b) approximates the probability of the positive class. Provide fit, predict_proba, and predict.

wpm 0acc 100%time 0:000 / 822

import numpy as np

class LogisticRegression:
    def __init__(self, lr: float = 0.01, epochs: int = 1000):
        self.lr = lr
        self.epochs = epochs
        self.weights = None
        self.bias = 0.0

    def _sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        for _ in range(self.epochs):
            z = X @ self.weights + self.bias
            preds = self._sigmoid(z)
            error = preds - y
            self.weights -= self.lr * (X.T @ error) / n_samples
            self.bias -= self.lr * error.mean()

    def predict_proba(self, X):
        return self._sigmoid(X @ self.weights + self.bias)

    def predict(self, X):
        return (self.predict_proba(X) >= 0.5).astype(int)

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