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.
Examples
Example 1:
Input:
predict_logistic(np.array([[1, 1], [2, 2], [-1, -1], [-2, -2]]), np.array([1, 1]), 0)Output:
[1 1 0 0]Explanation: Each sample's linear combination is computed using $z = Xw + b$. The sigmoid function is applied, and the output is thresholded at 0.5, resulting in binary predictions.
Starter Code
import numpy as np
def predict_logistic(X: np.ndarray, weights: np.ndarray, bias: float) -> np.ndarray:
"""
Implements binary classification prediction using Logistic Regression.
Args:
X: Input feature matrix (shape: N x D)
weights: Model weights (shape: D)
bias: Model bias
Returns:
Binary predictions (0 or 1)
"""
# Your code here
passPython3
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