我在使用SVM时遇到了一个问题，程序执行在model.fit(X_test, y_test)处卡住，这是SVM模型拟合的步骤。如何解决这个问题？这是我的代码：
# Make Predictions with Naive Bayes On The Iris Datasetimport collectionsfrom csv import readerfrom math import sqrt, exp, pifrom IPython.display import Imageimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdimport seaborn as sns; sns.set()from sklearn.cross_validation import train_test_split from sklearn.ensemble import ExtraTreesClassifierfrom sklearn.externals.six import StringIOfrom sklearn.feature_selection import SelectKBest, chi2from sklearn import datasets, metricsfrom sklearn.linear_model import LogisticRegressionfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrixfrom sklearn.naive_bayes import GaussianNBfrom sklearn import svmfrom sklearn import treefrom sklearn.tree import DecisionTreeClassifier, export_graphviz# Function to split the dataset def splitdataset(balance_data, column_count): # Separating the target variable X = balance_data.values[:, 1:column_count] Y = balance_data.values[:, 0] # Splitting the dataset into train and test X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.3, random_state = 100) return X, Y, X_train, X_test, y_train, y_test def importdata(): balance_data = pd.read_csv( 'dataExtended.txt', sep= ',') row_count, column_count = balance_data.shape # Printing the dataswet shape print ("Dataset Length: ", len(balance_data)) print ("Dataset Shape: ", balance_data.shape) print("Number of columns ", column_count) # Printing the dataset obseravtions print ("Dataset: ",balance_data.head()) balance_data['gold'] = balance_data['gold'].astype('category').cat.codes balance_data['Program'] = balance_data['Program'].astype('category').cat.codes return balance_data, column_count # Driver code def main(): print("hey") # Building Phase data,column_count = importdata() X, Y, X_train, X_test, y_train, y_test = splitdataset(data, column_count) #Create a svm Classifier model = svm.SVC(kernel='linear',probability=True) # Linear Kernel print('before fitting') model.fit(X_test, y_test) print('fitting over') predicted = model.predict(X_test) print('prediction over') print(metrics.classification_report(y_test, predicted)) print('classification over') print(metrics.confusion_matrix(y_test, predicted)) probs = model.predict_proba(X_test) probs_list = list(probs) y_pred=[None]*len(y_test) y_pred_list = list(y_pred) y_test_list = list(y_test) i=0 threshold=0.7 while i<len(probs_list): #print('probs ',probs_list[i][0]) if (probs_list[i][0]>=threshold) & (probs_list[i][1]<threshold): y_pred_list[i]=0 i=i+1 elif (probs_list[i][0]<threshold) & (probs_list[i][1]>=threshold): y_pred_list[i]=1 i=i+1 else: #print(y_pred[i]) #print('i==> ',i, ' probs length ', len(probs_list), ' ', len(y_pred_list), ' ', len(y_test_list)) y_pred_list.pop(i) y_test_list.pop(i) probs_list.pop(i) #print(y_pred_list) print('confusion matrix\n',confusion_matrix(y_test_list,y_pred_list)) print('classification report\n', classification_report(y_test_list,y_pred_list)) print('accuracy score', accuracy_score(y_test_list, y_pred_list)) print('Mean Absolute Error:', metrics.mean_absolute_error(y_test_list, y_pred_list)) print('Mean Squared Error:', metrics.mean_squared_error(y_test_list, y_pred_list)) print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test_list, y_pred_list)))if __name__=="__main__": main() 回答：
这很可能是由于在初始化模型时将probability参数设置为True导致的。正如您在文档中可以看到的：
probability: bool, default=False
是否启用概率估计。这必须在调用fit之前启用，会减慢该方法的速度，因为它在内部使用5倍交叉验证，并且predict_proba可能与predict不一致。
