Classification Visualizers

Classification models attempt to predict a target in a discrete space, that is assign an instance of dependent variables one or more categories. Classification score visualizers display the differences between classes as well as a number of classifier-specific visual evaluations. We currently have implemented four classifier evaluations:

• Classification Report: Presents the classification report of the classifier as a heatmap
• Confusion Matrix: Presents the confusion matrix of the classifier as a heatmap
• ROCAUC: Presents the graph of receiver operating characteristics along with area under the curve
• Class Balance: Displays the difference between the class balances and support

Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline.

# Classifier Evaluation Imports

from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance

• Classification Report
  • API Reference
• Confusion Matrix
  • API Reference
• ROCAUC
  • API Reference
• Class Balance
  • API Reference