How to check models recall score using cross validation in Python?
0

How to check models recall score using cross validation in Python?

This recipe helps you check models recall score using cross validation in Python
In [1]:
## How to check model's recall score using cross validation in Python
def Snippet_135():
    print()
    print(format('How to check model\'s recall 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="recall", cv = 7))
    mean_score = cross_val_score(dtree, X, y, scoring="recall", cv = 7).mean()
    std_score = cross_val_score(dtree, X, y, scoring="recall", cv = 7).std()
    print(); print(mean_score)
    print(); print(std_score)

Snippet_135()
********How to check model's recall score using cross validation in Python********

[0.92307692 0.91748252 0.92577031 0.91736695 0.93137255 0.91316527
 0.94397759]

0.9260025688597119

0.008419529122852532