繪製分類機率#

繪製不同分類器的分類機率。我們使用一個 3 類別資料集，並使用支持向量分類器、L1 和 L2 懲罰邏輯迴歸（多項多類別）、具有邏輯迴歸的一對多版本以及高斯過程分類對其進行分類。

線性 SVC 預設不是機率分類器，但在此範例中已啟用內建校準選項 (probability=True)。

具有一對多的邏輯迴歸並非開箱即用的多類別分類器。因此，在分離第 2 類和第 3 類時，它比其他估計器更困難。

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%

# 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()