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  • 通过直方图进行PCA准备

    import graphviz
    import mglearn
    from mpl_toolkits.mplot3d import Axes3D
    from sklearn.datasets import load_breast_cancer, make_blobs
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC
    from sklearn.tree import DecisionTreeClassifier, export_graphviz
    from IPython.display import display
    import matplotlib.pyplot as plt
    import numpy as np
    import matplotlib as mt
    import pandas as pd
    from sklearn.datasets import load_breast_cancer
    from sklearn.model_selection import train_test_split
    cancer = load_breast_cancer()
    
    # X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,
    #                                                     random_state=1)
    fig, axes = plt.subplots(15, 2, figsize=(10, 20))
    malignant = cancer.data[cancer.target == 0]
    benign = cancer.data[cancer.target == 1]
    ax = axes.ravel()
    # 直方图显示了数据值的分布情况
    for i in range(30):
        _, bins = np.histogram(cancer.data[:, i], bins=50)
        # 逐列取数
        ax[i].hist(malignant[:, i], bins=bins, color=mglearn.cm3(0), alpha=.5)
        ax[i].hist(benign[:, i], bins=bins, color=mglearn.cm3(2), alpha=.5)
        ax[i].set_title(cancer.feature_names[i])
        ax[i].set_yticks(())
    ax[0].set_xlabel("Feature magnitude")
    ax[0].set_ylabel("Frequency")
    ax[0].legend(["malignant", "benign"], loc="best")
    fig.tight_layout()
    plt.show()

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  • 原文地址:https://www.cnblogs.com/starcrm/p/11686898.html
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