绘制分类概率#

绘制不同分类器的分类概率。我们使用一个三类数据集，并使用支持向量分类器、L1 和 L2 惩罚逻辑回归（多项式多类）、基于逻辑回归的 One-Vs-Rest 版本以及高斯过程分类进行分类。

线性 SVC 默认情况下不是概率分类器，但它具有内置的校准选项，在本例中已启用 (probability=True)。

One-Vs-Rest 的逻辑回归不是开箱即用的多类分类器。因此，它在分离类别 2 和 3 方面比其他估计器更困难。

Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Probability

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%

# Author: Alexandre Gramfort <[email protected]>
# License: 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()

脚本总运行时间：（0 分钟 2.022 秒）