Scikit-Learn's model_selection check_cv in Python (with Examples)

The “sklearn.model_selection.check_cv” function is a utility function provided by scikit-learn (sklearn). It is used for validating and generating cross-validation objects.

Cross-validation is a technique used in machine learning to assess the performance of a predictive model on an independent dataset. It involves partitioning the data into multiple sets or “folds” and performing training and testing on these folds iteratively.

The “check_cv” function helps in the creation of cross-validation objects. It takes a parameter called “cv” which can be an integer representing the number of folds, a cross-validation object, or a strategy string. It then returns a validated cross-validator object. The returned object can be used with other methods in sklearn that require a cross-validation strategy, such as model selection and evaluation.

In summary, “sklearn.model_selection.check_cv” is a useful function in scikit-learn that facilitates the creation and validation of cross-validation objects, enabling efficient evaluation of machine learning models.

check_cv Python Examples

Example 1: Using KFold as a CV splitter


from sklearn.model_selection import check_cv
from sklearn.model_selection import KFold

cv = check_cv(KFold(n_splits=5))
print(cv)

KFold(n_splits=5, random_state=None, shuffle=False)

Example 2: Using custom CV splitter with check_cv


from sklearn.model_selection import check_cv

class CustomCV:
    def __init__(self, num_splits):
        self.num_splits = num_splits

    def get_n_splits(self, X=None, y=None, groups=None):
        return self.num_splits

    def split(self, X, y=None, groups=None):
        # Implement custom split logic here
        pass

cv = check_cv(CustomCV(num_splits=3))
print(cv)

<__main__.CustomCV object at 0x13f975250>

Check cv Visualization in Python

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import KFold, check_cv

# load iris dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

# define the number of folds
n_splits = 5

# create the cross-validation splits using KFold
kf = KFold(n_splits=n_splits)
cv = check_cv(cv=kf, y=y)

# visualize check_cv
fig, ax = plt.subplots()
for i, (_, test_index) in enumerate(cv.split(X, y)):
    ax.scatter(X[test_index, 0], X[test_index, 1], label=f"Fold {i+1}")

ax.legend()
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("check_cv Visualization")
plt.show()

Useful Python Libraries for check_cv

scikit-learn: sklearn.model_selection.check_cv, sklearn.model_selection.KFold, sklearn.model_selection.StratifiedKFold

numpy: numpy.array
pandas: pandas.DataFrame

Important Concepts in check_cv

Cross-validation

Training set
Test set
Stratified sampling

K-fold cross-validation
Random state
Shuffle

Train/test split
Scikit-learn
Machine learning

To Know Before You Learn check_cv?

Understanding of machine learning principles and algorithms
Familiarity with Python programming language
Knowledge of basic statistics and probability theory

Understanding of cross-validation techniques
Familiarity with the scikit-learn library (sklearn) and its components
Knowledge of data preprocessing and feature engineering techniques

Understanding of model evaluation metrics and techniques
Awareness of the importance of data splitting and validation in machine learning
Familiarity with the concept of k-fold cross-validation

Knowledge of the different types of model evaluation and validation strategies

What’s Next?

Cross-validation techniques
Hyperparameter tuning

Evaluation metrics
Feature selection
Ensemble methods

Deep learning algorithms
Neural networks
Natural language processing

Recommendation systems
Time series analysis

Conclusion

The sklearn.model_selection.check_cv function provides a convenient and flexible way to validate and cross-validate machine learning models. It allows for easy customization of the cross-validation strategy by accepting a CV splitter or an integer indicating the number of folds. Additionally, it supports stratified and shuffled splitting, making it suitable for a wide range of datasets and problems.

The function also offers built-in cross-validation iterators for common use cases, such as K-fold and stratified K-fold. Its integration with scikit-learn’s pipeline and grid search functionalities further enhances its usefulness. Overall, check_cv is a valuable tool for assessing the performance and generalizability of machine learning models.