Recipe: How to select features using best ANOVA F values in Python?
FEATURE EXTRACTION

How to select features using best ANOVA F values in Python?

This recipe helps you select features using best ANOVA F values in Python
In [1]:
## How to select features using best ANOVA F-values in Python
def Snippet_125():
    print()
    print(format('How to select features using best ANOVA F-values in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn.datasets import load_iris
    from sklearn.feature_selection import SelectKBest
    from sklearn.feature_selection import f_classif

    # Load iris data
    iris = load_iris()

    # Create features and target
    X = iris.data; print(); print(X)
    y = iris.target; print(); print(y)

    # Select Features With Best ANOVA F-Values
    # Create an SelectKBest object to select features with two best ANOVA F-Values
    fvalue_selector = SelectKBest(f_classif, k=2)
    # Apply the SelectKBest object to the features and target
    X_kbest = fvalue_selector.fit_transform(X, y)
    print(); print(X_kbest)

    # Show results
    print('Original number of features:', X.shape)
    print('Reduced number of features:', X_kbest.shape)

Snippet_125()
************How to select features using best ANOVA F-values in Python************

[[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]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]
 [4.8 3.  1.4 0.1]
 [4.3 3.  1.1 0.1]
 [5.8 4.  1.2 0.2]
 [5.7 4.4 1.5 0.4]
 [5.4 3.9 1.3 0.4]
 [5.1 3.5 1.4 0.3]
 [5.7 3.8 1.7 0.3]
 [5.1 3.8 1.5 0.3]
 [5.4 3.4 1.7 0.2]
 [5.1 3.7 1.5 0.4]
 [4.6 3.6 1.  0.2]
 [5.1 3.3 1.7 0.5]
 [4.8 3.4 1.9 0.2]
 [5.  3.  1.6 0.2]
 [5.  3.4 1.6 0.4]
 [5.2 3.5 1.5 0.2]
 [5.2 3.4 1.4 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.2 4.1 1.5 0.1]
 [5.5 4.2 1.4 0.2]
 [4.9 3.1 1.5 0.2]
 [5.  3.2 1.2 0.2]
 [5.5 3.5 1.3 0.2]
 [4.9 3.6 1.4 0.1]
 [4.4 3.  1.3 0.2]
 [5.1 3.4 1.5 0.2]
 [5.  3.5 1.3 0.3]
 [4.5 2.3 1.3 0.3]
 [4.4 3.2 1.3 0.2]
 [5.  3.5 1.6 0.6]
 [5.1 3.8 1.9 0.4]
 [4.8 3.  1.4 0.3]
 [5.1 3.8 1.6 0.2]
 [4.6 3.2 1.4 0.2]
 [5.3 3.7 1.5 0.2]
 [5.  3.3 1.4 0.2]
 [7.  3.2 4.7 1.4]
 [6.4 3.2 4.5 1.5]
 [6.9 3.1 4.9 1.5]
 [5.5 2.3 4.  1.3]
 [6.5 2.8 4.6 1.5]
 [5.7 2.8 4.5 1.3]
 [6.3 3.3 4.7 1.6]
 [4.9 2.4 3.3 1. ]
 [6.6 2.9 4.6 1.3]
 [5.2 2.7 3.9 1.4]
 [5.  2.  3.5 1. ]
 [5.9 3.  4.2 1.5]
 [6.  2.2 4.  1. ]
 [6.1 2.9 4.7 1.4]
 [5.6 2.9 3.6 1.3]
 [6.7 3.1 4.4 1.4]
 [5.6 3.  4.5 1.5]
 [5.8 2.7 4.1 1. ]
 [6.2 2.2 4.5 1.5]
 [5.6 2.5 3.9 1.1]
 [5.9 3.2 4.8 1.8]
 [6.1 2.8 4.  1.3]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [6.4 2.9 4.3 1.3]
 [6.6 3.  4.4 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.  5.  1.7]
 [6.  2.9 4.5 1.5]
 [5.7 2.6 3.5 1. ]
 [5.5 2.4 3.8 1.1]
 [5.5 2.4 3.7 1. ]
 [5.8 2.7 3.9 1.2]
 [6.  2.7 5.1 1.6]
 [5.4 3.  4.5 1.5]
 [6.  3.4 4.5 1.6]
 [6.7 3.1 4.7 1.5]
 [6.3 2.3 4.4 1.3]
 [5.6 3.  4.1 1.3]
 [5.5 2.5 4.  1.3]
 [5.5 2.6 4.4 1.2]
 [6.1 3.  4.6 1.4]
 [5.8 2.6 4.  1.2]
 [5.  2.3 3.3 1. ]
 [5.6 2.7 4.2 1.3]
 [5.7 3.  4.2 1.2]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.  1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.  2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.  5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.  5.8 2.2]
 [7.6 3.  6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2. ]
 [6.4 2.7 5.3 1.9]
 [6.8 3.  5.5 2.1]
 [5.7 2.5 5.  2. ]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.  5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.  2.2 5.  1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2. ]
 [7.7 2.8 6.7 2. ]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.  1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.  4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.  5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2. ]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.  6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.  3.  4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.  5.2 2.3]
 [6.3 2.5 5.  1.9]
 [6.5 3.  5.2 2. ]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]]

[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2]

[[1.4 0.2]
 [1.4 0.2]
 [1.3 0.2]
 [1.5 0.2]
 [1.4 0.2]
 [1.7 0.4]
 [1.4 0.3]
 [1.5 0.2]
 [1.4 0.2]
 [1.5 0.1]
 [1.5 0.2]
 [1.6 0.2]
 [1.4 0.1]
 [1.1 0.1]
 [1.2 0.2]
 [1.5 0.4]
 [1.3 0.4]
 [1.4 0.3]
 [1.7 0.3]
 [1.5 0.3]
 [1.7 0.2]
 [1.5 0.4]
 [1.  0.2]
 [1.7 0.5]
 [1.9 0.2]
 [1.6 0.2]
 [1.6 0.4]
 [1.5 0.2]
 [1.4 0.2]
 [1.6 0.2]
 [1.6 0.2]
 [1.5 0.4]
 [1.5 0.1]
 [1.4 0.2]
 [1.5 0.2]
 [1.2 0.2]
 [1.3 0.2]
 [1.4 0.1]
 [1.3 0.2]
 [1.5 0.2]
 [1.3 0.3]
 [1.3 0.3]
 [1.3 0.2]
 [1.6 0.6]
 [1.9 0.4]
 [1.4 0.3]
 [1.6 0.2]
 [1.4 0.2]
 [1.5 0.2]
 [1.4 0.2]
 [4.7 1.4]
 [4.5 1.5]
 [4.9 1.5]
 [4.  1.3]
 [4.6 1.5]
 [4.5 1.3]
 [4.7 1.6]
 [3.3 1. ]
 [4.6 1.3]
 [3.9 1.4]
 [3.5 1. ]
 [4.2 1.5]
 [4.  1. ]
 [4.7 1.4]
 [3.6 1.3]
 [4.4 1.4]
 [4.5 1.5]
 [4.1 1. ]
 [4.5 1.5]
 [3.9 1.1]
 [4.8 1.8]
 [4.  1.3]
 [4.9 1.5]
 [4.7 1.2]
 [4.3 1.3]
 [4.4 1.4]
 [4.8 1.4]
 [5.  1.7]
 [4.5 1.5]
 [3.5 1. ]
 [3.8 1.1]
 [3.7 1. ]
 [3.9 1.2]
 [5.1 1.6]
 [4.5 1.5]
 [4.5 1.6]
 [4.7 1.5]
 [4.4 1.3]
 [4.1 1.3]
 [4.  1.3]
 [4.4 1.2]
 [4.6 1.4]
 [4.  1.2]
 [3.3 1. ]
 [4.2 1.3]
 [4.2 1.2]
 [4.2 1.3]
 [4.3 1.3]
 [3.  1.1]
 [4.1 1.3]
 [6.  2.5]
 [5.1 1.9]
 [5.9 2.1]
 [5.6 1.8]
 [5.8 2.2]
 [6.6 2.1]
 [4.5 1.7]
 [6.3 1.8]
 [5.8 1.8]
 [6.1 2.5]
 [5.1 2. ]
 [5.3 1.9]
 [5.5 2.1]
 [5.  2. ]
 [5.1 2.4]
 [5.3 2.3]
 [5.5 1.8]
 [6.7 2.2]
 [6.9 2.3]
 [5.  1.5]
 [5.7 2.3]
 [4.9 2. ]
 [6.7 2. ]
 [4.9 1.8]
 [5.7 2.1]
 [6.  1.8]
 [4.8 1.8]
 [4.9 1.8]
 [5.6 2.1]
 [5.8 1.6]
 [6.1 1.9]
 [6.4 2. ]
 [5.6 2.2]
 [5.1 1.5]
 [5.6 1.4]
 [6.1 2.3]
 [5.6 2.4]
 [5.5 1.8]
 [4.8 1.8]
 [5.4 2.1]
 [5.6 2.4]
 [5.1 2.3]
 [5.1 1.9]
 [5.9 2.3]
 [5.7 2.5]
 [5.2 2.3]
 [5.  1.9]
 [5.2 2. ]
 [5.4 2.3]
 [5.1 1.8]]
Original number of features: (150, 4)
Reduced number of features: (150, 2)


Stuck at work?
Can't find the recipe you are looking for. Let us know and we will find an expert to create the recipe for you. Click here
Companies using this Recipe
1 developer from Vodafone
1 developer from ANAC
1 developer from Wipro
1 developer from HvH
1 developer from ICU Medical