Plot classification probability (original) (raw)
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Plot the classification probability for different classifiers. We use a 3 class dataset, and we classify it with a Support Vector classifier, L1 and L2 penalized logistic regression (multinomial multiclass), a One-Vs-Rest version with logistic regression, and Gaussian process classification.
Linear SVC is not a probabilistic classifier by default but it has a built-in calibration option enabled in this example (probability=True).
The logistic regression with One-Vs-Rest is not a multiclass classifier out of the box. As a result it has more trouble in separating class 2 and 3 than the other estimators.
Accuracy (train) for L1 logistic: 83.3% Accuracy (train) for L2 logistic (Multinomial): 82.7% Accuracy (train) for L2 logistic (OvR): 79.3% Accuracy (train) for Linear SVC: 82.0% Accuracy (train) for GPC: 82.7%
Authors: The scikit-learn developers
SPDX-License-Identifier: BSD-3-Clause
import matplotlib.pyplot as plt import numpy as np from matplotlib import cm
from sklearn import datasets from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF from sklearn.inspection import DecisionBoundaryDisplay from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.multiclass import OneVsRestClassifier from sklearn.svm import SVC
iris = datasets.load_iris() X = iris.data[:, 0:2] # we only take the first two features for visualization y = iris.target
n_features = X.shape[1]
C = 10 kernel = 1.0 * RBF([1.0, 1.0]) # for GPC
Create different classifiers.
classifiers = { "L1 logistic": LogisticRegression(C=C, penalty="l1", solver="saga", max_iter=10000), "L2 logistic (Multinomial)": LogisticRegression( C=C, penalty="l2", solver="saga", max_iter=10000 ), "L2 logistic (OvR)": OneVsRestClassifier( LogisticRegression(C=C, penalty="l2", solver="saga", max_iter=10000) ), "Linear SVC": SVC(kernel="linear", C=C, probability=True, random_state=0), "GPC": GaussianProcessClassifier(kernel), }
n_classifiers = len(classifiers)
fig, axes = plt.subplots( nrows=n_classifiers, ncols=len(iris.target_names), figsize=(3 * 2, n_classifiers * 2), ) for classifier_idx, (name, classifier) in enumerate(classifiers.items()): y_pred = classifier.fit(X, y).predict(X) accuracy = accuracy_score(y, y_pred) print(f"Accuracy (train) for {name}: {accuracy:0.1%}") for label in np.unique(y): # plot the probability estimate provided by the classifier disp = DecisionBoundaryDisplay.from_estimator( classifier, X, response_method="predict_proba", class_of_interest=label, ax=axes[classifier_idx, label], vmin=0, vmax=1, ) axes[classifier_idx, label].set_title(f"Class {label}") # plot data predicted to belong to given class mask_y_pred = y_pred == label axes[classifier_idx, label].scatter( X[mask_y_pred, 0], X[mask_y_pred, 1], marker="o", c="w", edgecolor="k" ) axes[classifier_idx, label].set(xticks=(), yticks=()) axes[classifier_idx, 0].set_ylabel(name)
ax = plt.axes([0.15, 0.04, 0.7, 0.02]) plt.title("Probability") _ = plt.colorbar( cm.ScalarMappable(norm=None, cmap="viridis"), cax=ax, orientation="horizontal" )
plt.show()
Total running time of the script: (0 minutes 1.387 seconds)
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