How to do variance thresholding in Python for feature selection?
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How to do variance thresholding in Python for feature selection?

How to do variance thresholding in Python for feature selection?

This recipe helps you do variance thresholding in Python for feature selection

0

Recipe Objective

To increse the score of the model we need the dataset that has high variance, so it will be good if we can select the features in the dataset which has variance more than a fix threshold.

This data science python source code does the following:
1. Uses Variance for selecting the best features.
2. Visualizes the final result

So this is the recipe on how we can do variance thresholding in Python for feature selection.

Step 1 - Import the library

from sklearn import datasets from sklearn.feature_selection import VarianceThreshold

We have only imported datasets to import the inbult dataset and VarienceThreshold.

Step 2 - Setting up the Data

We have imported inbuilt iris dataset and stored data in X and target in y. We have also used print statement to print first 8 rows of the dataset. iris = datasets.load_iris() X = iris.data print(X[0:7]) y = iris.target print(y[0:7])

Step 3 - Applying threshold on Variance

We have created an object for VarianceThreshold with parameter threshold in which we have to put the minimum value of variance we want in out dataset. Then we have used fit_transform to fit and transform the dataset. Finally we have printed the final dataset. thresholder = VarianceThreshold(threshold=.5) X_high_variance = thresholder.fit_transform(X) print(X_high_variance[0:7]) So in the output we can see that in final dataset we have 3 columns and in the initial dataset we have 4 columns which means the function have removed a column which has less variance.

[[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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