How to check models Average precision score using cross validation in Python?
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How to check models Average precision score using cross validation in Python?

This recipe helps you check models Average precision score using cross validation in Python
In [1]:
## How to check model's Average precision score using cross validation in Python
def Snippet_137():
    print()
    print(format('How to check model\'s Average precision score using cross validation in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn.model_selection import cross_val_score
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.datasets import make_classification

    # Generate features matrix and target vector
    X, y = make_classification(n_samples = 10000,
                               n_features = 3,
                               n_informative = 3,
                               n_redundant = 0,
                               n_classes = 2,
                               random_state = 42)

    # Create Decision Tree model
    dtree = DecisionTreeClassifier()

    # Cross-validate model using accuracy
    print(); print(cross_val_score(dtree, X, y, scoring="average_precision", cv = 7))
    mean_score = cross_val_score(dtree, X, y, scoring="average_precision", cv = 7).mean()
    std_score = cross_val_score(dtree, X, y, scoring="average_precision", cv = 7).std()
    print(); print(mean_score)
    print(); print(std_score)

Snippet_137()
**How to check model's Average precision score using cross validation in Python***

[0.88845957 0.88047835 0.9132533  0.89760678 0.89968758 0.89812809
 0.88732474]

0.8953026160280041

0.0105346251221406