Classification Accuracy
~8 mincode completion
Implement that returns the fraction of correct predictions.
Examples
3 correct out of 5
- Input
- accuracy([1, 0, 1, 1, 0], [1, 0, 0, 1, 1])
- Output
- 0.6
- Input
- accuracy([1, 1, 0, 0], [1, 1, 0, 0])
- Output
- 1
- Input
- accuracy([0, 0, 0], [1, 1, 1])
- Output
- 0
Hints
Hint 1
does the sum and the division in one step.
Hint 2
Watch for this: counted sum not mean.
Requirements
y_true: Ground truth labels, shape (m,)y_pred: Predicted labels, shape (m,)Return Fraction of correct predictions in [0, 1].
Constraints
Allowed library: NumPy only
Time limit: 200 ms, Memory: 64 MB
Where this shows up
~8 min
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8 employers weight this skill
3 AI product companies, 2 frontier labs, 1 health and bio company, 1 big tech firm, 1 autonomy company. Top match scores 93.
Python
import numpy as np
def accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""
Compute classification accuracy.
Args:
y_true: Ground truth labels, shape (m,)
y_pred: Predicted labels, shape (m,)
Returns:
Fraction of correct predictions in [0, 1].
"""
# YOUR CODE HERE
pass