Recipe: How to do variance thresholding in Python for feature selection?
FEATURE EXTRACTION

How to do variance thresholding in Python for feature selection?

This recipe helps you do variance thresholding in Python for feature selection
In [1]:
## How to do variance thresholding in Python for feature selection
def Snippet_130():
    print()
    print(format('How to do variance thresholding in Python for feature selection','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn.feature_selection import VarianceThreshold

    # Load iris data
    iris = datasets.load_iris()

    # Create features and target
    X = iris.data; print(); print(X[0:7])
    y = iris.target; print(); print(y[0:7])

    # Create VarianceThreshold object with a variance with a threshold of 0.5
    thresholder = VarianceThreshold(threshold=.5)

    # Conduct variance thresholding
    X_high_variance = thresholder.fit_transform(X)

    # View first five rows with features with variances above threshold
    print(); print(X_high_variance[0:7])

Snippet_130()
*********How to do variance thresholding in Python for feature selection**********

[[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]]

[0 0 0 0 0 0 0]

[[5.1 1.4 0.2]
 [4.9 1.4 0.2]
 [4.7 1.3 0.2]
 [4.6 1.5 0.2]
 [5.  1.4 0.2]
 [5.4 1.7 0.4]
 [4.6 1.4 0.3]]


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