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?

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

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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

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