This plot immediately reveals which model dominates. The curve that is highest and furthest to the left is superior.
: Supports multiple programming languages, including Go and Java. Typical Use Cases roc toolkit
When you install the toolkit, you typically get three main helper tools that do the heavy lifting: This plot immediately reveals which model dominates
import numpy as np from sklearn.metrics import roc_curve, roc_auc_score, RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification y_test = train_test_split(X
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) model = RandomForestClassifier() model.fit(X_train, y_train) y_scores = model.predict_proba(X_test)[:, 1]
[ J = \textTPR - \textFPR ]