# 准确率importnumpyasnp from sklearn.metricsimportaccuracy_score y_pred=[0,2,1,3,9,9,8,5,8]y_true=[0,1,2,3,2,6,3,5,9]accuracy_score(y_true,y_pred)Out[127]:0.33333333333333331accuracy_score(y_true,y_pred,normalize=False)# 类似海明距离,每个类别求准确后,再求微平均 Out[128]:3...
from sklearn import svm #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset # Create SVM classification object model = svm.svc() # there is various option associated with it, this is simple for classification. You can refer link,...
通过核技巧,SVM可以有效地处理复杂的非线性问题,使其成为一种非常强大且灵活的机器学习工具。在实际应用中,选择合适的核函数和调整核函数的参数(如RBF核的γ)是获得良好性能的关键。 from sklearn.svm import SVC from sklearn.datasets import make_blobs from sklearn.model_selection import train_test_split fro...
from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # ...
除了在Matlab中使用PRTools工具箱中的svm算法,Python中一样可以使用支持向量机做分类。因为Python中的sklearn库也集成了SVM算法,本文的运行环境是Pycharm。 一、导入sklearn算法包 Scikit-Learn库已经实现了所有基本机器学习的算法,具体使用详见官方文档说明:http://scikit-learn.org/stable/auto_examples/index.html#su...
训练集训练分类器svm,并用测试集来测试准确率。 train_data,test_data,train_label,test_label=sklearn.model_selection.train_test_split(x,y,random_state=1,train_size=0.6,test_size=0.4) 参数说明: 1.x:特征值 2.y:标签 3.random_state:是随机数的种子。
from sklearn import svm from sklearn import metrics from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler#预处理数据 1. 2. 3. 4. 5. 6. 7. 8. (2)加载数据集,然后查看样本特征和特征值和样本特征值的描述信息 ...
/usr/bin/python#-*- coding:utf-8 -*-importnumpy as npfromsklearnimportsvmimportmatplotlib as mplimportmatplotlib.colorsimportmatplotlib.pyplot as pltdefshow_accuracy(a, b): acc= a.ravel() ==b.ravel()print('正确率:%.2f%%'% (100 * float(acc.sum()) /a.size))if__name__=="__...