非线性支持向量机在ScikitLearn中的错误

Question

我有一个代码尝试使用非线性支持向量机（RBF核）。在

raw_data1 = open("/Users/prateek/Desktop/Programs/ML/Dataset.csv")
raw_data2 = open("/Users/prateek/Desktop/Programs/ML/Result.csv")

dataset1 = np.loadtxt(raw_data1,delimiter=",")
result1 = np.loadtxt(raw_data2,delimiter=",")

clf = svm.NuSVC(kernel='rbf')
clf.fit(dataset1,result1)

然而，当我试图拟合时，我得到了错误

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Link for Results.csv

Link for dataset

出现这种错误的原因是什么？在

Answer 1

正如文档中指出的，nu参数是“训练错误分数的上界和支持向量分数的下界”。在

所以，每当你试图拟合你的数据而这个界限不能满足时，优化问题就变得不可行了。所以你的错误。在

事实上，我从1.循环到0.1（以十进制单位递减），仍然得到错误，然后用0.01进行尝试，没有引起任何投诉。但当然，你应该检查用这个值拟合模型的结果，检查预测的准确性是否可以接受。在

Update: actually I was curious and splitted your dataset to validate, output was 69% accuracy (also I think your training set might be very little)

为了重现性，这里，我进行了快速测试：

from sklearn import svm
import numpy as np 
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score

raw_data1 = open("Dataset.csv")
raw_data2 = open("Result.csv")
dataset1 = np.loadtxt(raw_data1,delimiter=",")
result1 = np.loadtxt(raw_data2,delimiter=",")

clf = svm.NuSVC(kernel='rbf',nu=0.01)
X_train, X_test, y_train, y_test = train_test_split(dataset1,result1, test_size=0.25, random_state=42)
clf.fit(X_train,y_train)
y_pred = clf.predict(X_test)
accuracy_score(y_test, y_pred, normalize=True, sample_weight=None)