5. Visualizations (original) (raw)
Scikit-learn defines a simple API for creating visualizations for machine learning. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. We provide Display classes that expose two methods for creating plots: from_estimator andfrom_predictions. The from_estimator method will take a fitted estimator and some data (X and y) and create a Display object. Sometimes, we would like to only compute the predictions once and one should use from_predictionsinstead. In the following example, we plot a ROC curve for a fitted support vector machine:
from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import RocCurveDisplay from sklearn.datasets import load_wine
X, y = load_wine(return_X_y=True) y = y == 2 # make binary X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) svc = SVC(random_state=42) svc.fit(X_train, y_train)
svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
The returned svc_disp object allows us to continue using the already computed ROC curve for SVC in future plots. In this case, the svc_disp is aRocCurveDisplay that stores the computed values as attributes called roc_auc, fpr, and tpr. Be aware that we could get the predictions from the support vector machine and then use from_predictionsinstead of from_estimator. Next, we train a random forest classifier and plot the previously computed roc curve again by using the plot method of theDisplay object.
import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=10, random_state=42) rfc.fit(X_train, y_train)
ax = plt.gca() rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8) svc_disp.plot(ax=ax, alpha=0.8)
Notice that we pass alpha=0.8 to the plot functions to adjust the alpha values of the curves.
Examples
- ROC Curve with Visualization API
- Advanced Plotting With Partial Dependence
- Visualizations with Display Objects
- Comparison of Calibration of Classifiers