Class Prediction Error Visualizer

The ClassPredictionError visualizer is a ScoreVisualizer that takes a fitted scikit-learn classifier and a set of test X and y values and returns a stacked bar graph showing a color-coded break down of predicted classes compared to their actual classes. This visualizer provides a way to quickly understand how good your classifier is at predicting the right classes.

Below is an example with the visualizer



In [1]:

    
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

from yellowbrick.classifier import ClassPredictionError

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split as tts
from sklearn.ensemble import RandomForestClassifier



In [2]:

    
# We create our own classification dataset
# The data set contains 5 classess and 1000 samples
# We use RandomForest for modeling the data
# I came up with arbitrary names for the classes

X, y = make_classification(n_samples=1000, n_classes=5,
                           n_informative=3, n_clusters_per_class=1)

# Perform 80/20 training/test split
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.20,
                                       random_state=42)

# Pass in model and classes to ClassPredictionError
visualizer = ClassPredictionError(RandomForestClassifier(),
                                  classes=['apple', 'kiwi', 'pear',
                                           'banana', 'orange'])
# Fit the model
visualizer.fit(X_train, y_train)

# Use test data to create visualization
visualizer.score(X_test, y_test)

# Display visualization
visualizer.show()
plt.show()



In [ ]: