AUCμ is a performance measure for multi-class classification models and it is an extension of the standard two-class area under the receiver operating characteristic curve (AUC-ROC).
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Dependencies for this Python script include: Python, scikit-learn, and the scipy stack.
Simply place auc_mu.py in any directory that is in your Python import path.
import auc_mu import numpy as np from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier as RFC from sklearn.model_selection import train_test_split as tts data = load_iris() X = data.data y = data.target np.random.seed(0) X_train, X_test, y_train, y_test = tts(X, y, test_size=.3) clf = RFC(n_estimators=100) clf.fit(X_train, y_train) y_score = clf.predict_proba(X_test) auc_mu.auc_mu(y_test, y_score) >>> 0.99326599
Additional information regarding use of an alternative partition matrix or weight matrix is contained in the auc_mu.auc_mu Docstring.
- Ross Kleiman
This project is licensed under the MIT License - see the LICENSE.md file for details