多层感知机中变化的正则化

在合成数据集上比较正则化参数“alpha”的不同值。绘图显示，不同的 alpha 值会产生不同的决策函数。

Alpha 是正则化项（又称惩罚项）的一个参数，通过约束权重的大小来对抗过拟合。增大 alpha 可能会通过鼓励更小的权重来修正高方差（过拟合的迹象），从而使决策边界图的曲率减小。类似地，减小 alpha 可能会通过鼓励更大的权重来修正高偏差（欠拟合的迹象），从而可能产生更复杂的决策边界。

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

from sklearn.datasets import make_circles, make_classification, make_moons
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

h = 0.02  # step size in the mesh

alphas = np.logspace(-1, 1, 5)

classifiers = []
names = []
for alpha in alphas:
    classifiers.append(
        make_pipeline(
            StandardScaler(),
            MLPClassifier(
                solver="lbfgs",
                alpha=alpha,
                random_state=1,
                max_iter=2000,
                early_stopping=True,
                hidden_layer_sizes=[10, 10],
            ),
        )
    )
    names.append(f"alpha {alpha:.2f}")

X, y = make_classification(
    n_features=2, n_redundant=0, n_informative=2, random_state=0, 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,
]

figure = plt.figure(figsize=(17, 9))
i = 1
# iterate over datasets
for X, y in datasets:
    # split into training and test part
    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
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

    # just plot the dataset first
    cm = plt.cm.RdBu
    cm_bright = ListedColormap(["#FF0000", "#0000FF"])
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
    # Plot the training points
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
    # and testing points
    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.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.fit(X_train, y_train)
        score = clf.score(X_test, y_test)

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, x_max] x [y_min, y_max].
        if hasattr(clf, "decision_function"):
            Z = clf.decision_function(np.column_stack([xx.ravel(), yy.ravel()]))
        else:
            Z = clf.predict_proba(np.column_stack([xx.ravel(), yy.ravel()]))[:, 1]

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)

        # Plot also the training points
        ax.scatter(
            X_train[:, 0],
            X_train[:, 1],
            c=y_train,
            cmap=cm_bright,
            edgecolors="black",
            s=25,
        )
        # and testing points
        ax.scatter(
            X_test[:, 0],
            X_test[:, 1],
            c=y_test,
            cmap=cm_bright,
            alpha=0.6,
            edgecolors="black",
            s=25,
        )

        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        ax.set_title(name)
        ax.text(
            xx.max() - 0.3,
            yy.min() + 0.3,
            f"{score:.3f}".lstrip("0"),
            size=15,
            horizontalalignment="right",
        )
        i += 1

figure.subplots_adjust(left=0.02, right=0.98)
plt.show()

相关示例

特征离散化

特征离散化

分类器比较

分类器比较

鸢尾花数据集上的高斯过程分类 (GPC)

鸢尾花数据集上的高斯过程分类 (GPC)

鸢尾花数据集上半监督分类器与 SVM 的决策边界

鸢尾花数据集上半监督分类器与 SVM 的决策边界