Cross-Validation
~12 mincode completion
Write cv_mean_score(X, y, folds) that runs cross-validation on a (with random_state=0) and returns the mean score as a float.
Examples
A perfectly separable dataset scores 1.0 across every fold
- Input
- cv_mean_score([[0], [1], [2], [8], [9], [10]], [0, 0, 0, 1, 1, 1], 3)
- Output
- 1
The same data with two folds still scores 1.0
- Input
- cv_mean_score([[0], [1], [2], [8], [9], [10]], [0, 0, 0, 1, 1, 1], 2)
- Output
- 1
Hints
Hint 1
Work directly with the arguments X, y, folds and return the result rather than printing it.
Hint 2
Make sure you are not returning all fold scores.
Requirements
X: 2-D feature arrayy: 1-D label arrayfolds: how many folds, e.g. 3Return the mean of the per-fold scores, as a float.
Constraints
Allowed library: NumPy only
Time limit: 200 ms, Memory: 64 MB
Try similar problems(4)
Where this shows up
~12 min
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Python
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
def cv_mean_score(X, y, folds):
"""
Mean cross-validated accuracy.
Args:
X: 2-D feature array
y: 1-D label array
folds: how many folds, e.g. 3
Returns:
The mean of the per-fold scores, as a float.
"""
# YOUR CODE HERE
pass