本示例旨在展示不同分类器在决策边界上的特性。需要注意的是，这些示例所传达的直觉并不一定适用于真实数据集。特别是在高维空间中，数据更容易线性分离，分类器如朴素贝叶斯和线性SVM的简单性可能会比其他分类器带来更好的泛化性能。
图表显示了实心颜色的训练点和半透明的测试点。右下角显示了测试集上的分类准确度。
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
from sklearn.datasets import make_circles, make_classification, make_moons
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
定义了分类器的名称和实例，以及用于生成数据集的函数。然后，生成了两个特征、无冗余特征、两个信息特征的数据集，并对其进行了一些随机扰动。
names = [
"Nearest Neighbors",
"Linear SVM",
"RBF SVM",
"Gaussian Process",
"Decision Tree",
"Random Forest",
"Neural Net",
"AdaBoost",
"Naive Bayes",
"QDA",
]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025, random_state=42),
SVC(gamma=2, C=1, random_state=42),
GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42),
DecisionTreeClassifier(max_depth=5, random_state=42),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1, random_state=42),
MLPClassifier(alpha=1, max_iter=1000, random_state=42),
AdaBoostClassifier(random_state=42),
GaussianNB(),
QuadraticDiscriminantAnalysis(),
]
X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [
make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
linearly_separable,
]
创建了一个图形界面，用于展示数据集和分类器的决策边界。对于每个数据集，都会绘制输入数据，然后对每个分类器进行训练和测试，并展示其决策边界和分类准确度。
figure = plt.figure(figsize=(27, 9))
i = 1 # iterate over datasets
for ds_cnt, ds in enumerate(datasets):
X, y = ds
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(["#FF0000", "#0000FF"])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data")
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
# Plot the testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k")
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
i += 1
# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf = make_pipeline(StandardScaler(), clf)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
DecisionBoundaryDisplay.from_estimator(
clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
)
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
# Plot the testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
if ds_cnt == 0:
ax.set_title(name)
ax.text(x_max - 0.3, y_min + 0.3, ("%.2f" % score).lstrip("0"), size=15, horizontalalignment="right")
i += 1
plt.tight_layout()
plt.show()
脚本的总运行时间为：(0分钟 2.491秒)。