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 perform with
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 - Regression¶
Mutual information between features and the dependent variable is calculated with
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 = True # Instantiate the visualizer visualizer = FeatureCorrelation(method='mutual_info-regression', labels=features) visualizer.fit(X, y, discrete_features=discrete, random_state=0) visualizer.show()
Mutual Information - Classification¶
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
Feature Correlation to Dependent Variable Visualizer.
FeatureCorrelation(ax=None, method='pearson', labels=None, sort=False, feature_index=None, feature_names=None, color=None, **kwargs)¶
Displays the correlation between features and dependent variables.
This visualizer can be used side-by-side with
yellowbrick.features.JointPlotVisualizerthat plots a feature against the target and shows the distribution of each via a histogram on each axis.
- axmatplotlib Axes, default: None
The axis to plot the figure on. If None is passed in the current axes will be used (or generated if required).
- methodstr, default: ‘pearson’
The method to calculate correlation between features and target. Options include:
‘pearson’, which uses
‘mutual_info-regression’, which uses
‘mutual_info-classification’, which uses
- labelslist, default: None
A list of feature names to use. If a DataFrame is passed to fit and features is None, feature names are selected as the column names.
- sortboolean, default: False
If false, the features are are not sorted in the plot; otherwise features are sorted in ascending order of correlation.
A list of feature index to include in the plot.
- feature_nameslist of feature names
A list of feature names to include in the plot. Must have labels or the fitted data is a DataFrame with column names. If feature_index is provided, feature_names will be ignored.
- color: string
Specify color for barchart
Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers.
>>> viz = FeatureCorrelation() >>> viz.fit(X, y) >>> viz.show()
The feature labels
Correlation between features and dependent variable.
Draws the feature correlation to dependent variable, called from fit.
Finalize the drawing setting labels and title.
fit(self, X, y, **kwargs)¶
Fits the estimator to calculate feature correlation to dependent variable.
- Xndarray or DataFrame of shape n x m
A matrix of n instances with m features
- yndarray or Series of length n
An array or series of target or class values
Keyword arguments passed to the fit method of the estimator.
The fit method must always return self to support pipelines.