python之实战----线性判别分析（LDA）战iris

最新推荐文章于 2024-10-20 21:33:37 发布

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最新推荐文章于 2024-10-20 21:33:37 发布 · 5.4k 阅读

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本文通过实例展示了使用Python进行线性判别分析（LDA）在Iris数据集上的应用。在进行LDA预测后，遇到DataConversionWarning，提示需要将二维的y转换为一维。解决方法是对y进行ravel()转换，以适应fit函数的输入要求。

LDA预测

# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt  
import numpy as np  
from sklearn.model_selection import train_test_split   
from sklearn import datasets, discriminant_analysis
def laod_data():
    iris=datasets.load_iris()
    X_train=iris.data
    y_train=iris.target
    return train_test_split(X_train,y_train,
    test_size=0.25,random_state=0,stratify=y_train)#stratify分层
def test_LinearDiscriminantAnalysis(*data):
    X_train,X_test,y_train,y_test=data
    lda=discriminant_analysis.LinearDiscriminantAnalysis()
    lda.fit(X_train,y_train)
    print('Coefficients:%s, intercept %s'%(lda.coef_,lda.intercept_))
    print("Score:%.2f"%lda.score(X_test,y_test))
if __name__=='__main__':
    X_train,X_test,y_train,y_test=laod_data() 
    test_LinearDiscriminantAnalysis(X_train,X_test,y_train,y_test)