第十五周（sklearn）

最新推荐文章于 2025-09-30 14:42:01 发布

原创最新推荐文章于 2025-09-30 14:42:01 发布 · 241 阅读

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本文通过使用sklearn库创建了一个人工数据集，并利用交叉验证的方法对比了三种不同的机器学习分类器：高斯朴素贝叶斯(GuassianNB)、支持向量机(SVC)及随机森林(RandomForestClassifier)的性能。实验结果显示随机森林分类器的准确度较高。

首先是创建数据集和split 数据集

import sklearn
from sklearn import datasets
from sklearn import cross_validation
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
dataset = datasets.make_classification(n_samples=1000,n_features=10)
data = dataset[0]
target = dataset[1]
kf = cross_validation.KFold(len(data),n_folds=10,shuffle=True)

然后是Guassian NB 算法训练

# Guassian NB
i = 1
for train_i,test_i in kf:
    x_train,y_train = data[train_i],target[train_i]
    x_test,y_test = data[test_i],target[test_i]
    clf = GaussianNB()
    clf.fit(x_train,y_train)
    pred = clf.predict(x_test)
    print("Group:",i)
    i += 1
    print("Accuracy：", metrics.accuracy_score(y_test, pred))
    print("F1-score:", metrics.f1_score(y_test, pred))
    print("AUC ROC:",metrics.roc_auc_score(y_test, pred))

SVC

#SVC
for c in [1e-02, 1e-01, 1e00, 1e01, 1e02]:
    i = 1
    for train_index,test_index in kf:
        x_train,y_train = data[train_index],target[train_index]
        x_test,y_test = data[test_index],target[test_index]
        clf = SVC(C=c,kernel='rbf',gamma=0.1)
        clf.fit(x_train,y_train)
        pred = clf.predict(x_test)
        print("Group:",i)
        i += 1
        print("C = ",c)
        print("Accuracy：", metrics.accuracy_score(y_test, pred))
        print("F1-score:", metrics.f1_score(y_test, pred))
        print("AUC ROC:",metrics.roc_auc_score(y_test, pred))

输出较长，这里不全部给出截图

RandomForestClassifier

for n in [10, 100, 1000]:
    i = 1
    for train_index,test_index in kf:
        x_train,y_train = data[train_index],target[train_index]
        x_test,y_test = data[test_index],target[test_index]
        clf = RandomForestClassifier(n_estimators=n)
        clf.fit(x_train,y_train)
        pred = clf.predict(x_test)
        print("Group:",i)
        i += 1
        print("n_estimators = ",n)
        print("Accuracy：", metrics.accuracy_score(y_test, pred))
        print("F1-score:", metrics.f1_score(y_test, pred))
        print("AUC ROC:",metrics.roc_auc_score(y_test, pred))