我正在尝试使用 SVM 模型进行机器学习，但它没有完成

Question

我正在尝试在数据集上使用 SVM 模型。它在train中有125973条记录，在test中有22544条记录。它永远保持计算，永远不会完成。谁能帮我。以下是我目前的python代码..

import pandas as pd
import sklearn.svm as s
import sklearn.metrics as m
import sklearn.preprocessing as pp

traindata = pd.read_csv("E:\\a-train.csv")

testdata = pd.read_csv("E:\\a-test.csv")

for col in traindata.columns:
    if traindata[col].dtype == type(object):
        le = pp.LabelEncoder()
        traindata[col] = le.fit_transform(traindata[col])
        
for col in testdata.columns:
    if testdata[col].dtype == type(object):
        le = pp.LabelEncoder()
        testdata[col] = le.fit_transform(testdata[col])

countS = 0

featsTrain = traindata.values[:,0:13]

featsTest = testdata.values[:,0:13]

lblsTrain = traindata.values[:,13]

lblsTest = testdata.values[:,13]

modelS = s.SVC(cache_size = 7000)

modelS.fit(featsTrain, lblsTrain)

lblsPredS = modelS.predict(featsTest)

for a,b in zip(lblsTest, lblsPredS):
   if a == b:
      countS += 1

accS = (round(countS/(len(featsTest)), 3)) * 100

print( m.confusion_matrix(lblsTest, lblsPredS) )

print(m.classification_report(lblsTest, lblsPredS))

print("\nAccuracy = ", accS, "%")

Answer 1

请仔细阅读这篇文章，这可能会解决您的问题/用例：

SVC classifier taking too much time for training