Logistic RegressionMedium
Logistic Classifier Predictions
~15 mincode completion
Implement logistic_predict(X, weights, threshold) that returns an integer array of 0s and 1s.
Examples
Zero input: sigmoid=0.5, above default threshold
- Input
- logistic_predict([[0], [10], [-10]], [1], 0.5)
- Output
- [1, 1, 0]
Negative scores: all predict 0
- Input
- logistic_predict([[1, 2], [3, 4]], [0.5, -1], 0.5)
- Output
- [0, 0]
Raised threshold makes prediction stricter
- Input
- logistic_predict([[0], [0], [10]], [1], 0.8)
- Output
- [0, 0, 1]
Hints
Hint 1
applies elementwise, so negate the whole array and exponentiate it in one go.
Hint 2
Do not forget to threshold comparison. That step is easy to skip.
Requirements
X: Feature matrix of shape (m, n)weights: Weight vector of shape (n,)threshold: Probability cutoff for predicting class 1Return Integer array of shape (m,) with values 0 or 1.
Constraints
Allowed library: NumPy only
Time limit: 200 ms, Memory: 64 MB
Try similar problems(1)
Where this shows up
~15 min
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Python
import numpy as np
def logistic_predict(X: np.ndarray, weights: np.ndarray, threshold: float) -> np.ndarray:
"""
Predict binary labels using logistic regression.
Args:
X: Feature matrix of shape (m, n)
weights: Weight vector of shape (n,)
threshold: Probability cutoff for predicting class 1
Returns:
Integer array of shape (m,) with values 0 or 1.
"""
# YOUR CODE HERE
pass