Iris数据集LDA和PCA 2D投影的比较#

Iris数据集代表3种Iris花（Setosa，Versicolour和Virginica），具有4个属性：萼片长度，萼片宽度，花瓣长度和花瓣宽度。

应用于此数据的主成分分析（PCA）识别了占数据中最大方差的属性组合（主成分或特征空间中的方向）。在这里，我们将不同的样本绘制在2个第一主成分上。

线性鉴别分析（LDA）尝试识别导致最大方差的属性 between classes .特别是，与PCA相反，LDA是一种使用已知类标签的监督方法。

explained variance ratio (first two components): [0.92461872 0.05306648]

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

import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

iris = datasets.load_iris()

X = iris.data
y = iris.target
target_names = iris.target_names

pca = PCA(n_components=2)
X_r = pca.fit(X).transform(X)

lda = LinearDiscriminantAnalysis(n_components=2)
X_r2 = lda.fit(X, y).transform(X)

# Percentage of variance explained for each components
print(
    "explained variance ratio (first two components): %s"
    % str(pca.explained_variance_ratio_)
)

plt.figure()
colors = ["navy", "turquoise", "darkorange"]
lw = 2

for color, i, target_name in zip(colors, [0, 1, 2], target_names):
    plt.scatter(
        X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=0.8, lw=lw, label=target_name
    )
plt.legend(loc="best", shadow=False, scatterpoints=1)
plt.title("PCA of IRIS dataset")

plt.figure()
for color, i, target_name in zip(colors, [0, 1, 2], target_names):
    plt.scatter(
        X_r2[y == i, 0], X_r2[y == i, 1], alpha=0.8, color=color, label=target_name
    )
plt.legend(loc="best", shadow=False, scatterpoints=1)
plt.title("LDA of IRIS dataset")

plt.show()

Total running time of the script: （0分0.143秒）