This visualizer calculates Pearson correlation coefficients and mutual information between features and the dependent variable. This visualization can be used in feature selection to identify features with high correlation or large mutual information with the dependent variable.
The default calculation is Pearson correlation, which is performed with scipy.stats.pearsonr.
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from sklearn import datasets from yellowbrick.target import FeatureCorrelation
# Load the regression dataset data = datasets.load_diabetes() X, y = data['data'], data['target']
# Create a list of the feature names features = np.array(data['feature_names'])
# Instantiate the visualizer visualizer = FeatureCorrelation(labels=features)
visualizer.fit(X, y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure
Mutual information between features and the dependent variable is calculated with sklearn.feature_selection.mutual_info_classif when method='mutual_info-classification' and mutual_info_regression when method='mutual_info-regression'. It is very important to specify discrete features when calculating mutual information because the calculation for continuous and discrete variables are different. See scikit-learn documentation for more details.
from sklearn import datasets from yellowbrick.target import FeatureCorrelation
# Load the regression dataset data = datasets.load_diabetes() X, y = data['data'], data['target']
# Create a list of the feature names features = np.array(data['feature_names'])
# Create a list of the discrete features discrete = [False for _ in range(len(features))] discrete[1] = True
# Instantiate the visualizer visualizer = FeatureCorrelation(method='mutual_info-regression', labels=features)
visualizer.fit(X, y, discrete_features=discrete, random_state=0) visualizer.show()
By fitting with a pandas DataFrame, the feature labels are automatically obtained from the column names. This visualizer also allows sorting of the bar plot according to the calculated mutual information (or Pearson correlation coefficients) and selecting features to plot by specifying the names of the features or the feature index.
import pandas as pd
from sklearn import datasets from yellowbrick.target import FeatureCorrelation
# Load the regression dataset data = datasets.load_wine() X, y = data['data'], data['target'] X_pd = pd.DataFrame(X, columns=data['feature_names'])
# Create a list of the features to plot features = ['alcohol', 'ash', 'hue', 'proline', 'total_phenols']
# Instaniate the visualizer visualizer = FeatureCorrelation( method='mutual_info-classification', feature_names=features, sort=True )
visualizer.fit(X_pd, y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure
The same functionality above can be achieved with the associated quick method feature_correlation. This method will build the FeatureCorrelation object with the associated arguments, fit it, then (optionally) immediately show it
import numpy as np from sklearn import datasets import matplotlib.pyplot as plt from yellowbrick.target.feature_correlation import feature_correlation
#Load the diabetes dataset data = datasets.load_iris() X, y = data['data'], data['target']
features = np.array(data['feature_names']) visualizer = feature_correlation(X, y, labels=features) plt.tight_layout()
yellowbrick.target.feature_correlation