h= .02#step size in the meshnames= ["Nearest Neighbors","Linear SVM","RBF SVM","Gaussian Process","Decision Tree","Random Forest","Neural Net","AdaBoost","Naive Bayes","QDA"] classifiers=[ KNeighborsClassifier(3), SVC(kernel="linear", C=0.025), SVC(gamma=2, C=1), GaussianProce...
format(a=dt_clf.score(X_train, y_train), b=dt_clf.score(X_test, y_test)))#采用支持向量机svc_clf = SVC(C=1.0, kernel='rbf', degree=3, gamma='scale') svc_clf.fit(X_train, y_train) y_predict_svc=svc_clf.predict(X_test)print('逻辑回归的训练集分数:{a:.2%}, 测试集分数:{...
svm_param.svm_type = EPSILON_SVR;// default for frac.libsvr is EPSILON_SVR, not C_SVCsvm_param.kernel_type = LINEAR;// noto changed default from RBFsvm_param.degree =3; svm_param.gamma =0;// 1/num_featuressvm_param.coef0 =0; svm_param.nu =0.5; svm_param.cache_size =100; sv...
# also tested this: # svm.SVC(kernel='linear', C=1.0), GaussianNB() # doesn't improve and takes long #running crossvalidation score on all classifiers for clf in classifiers: score = cross_val_score(clf, X, y, cv=cv) print "%s Accuracy: %0.2f (+/- %0.2f) " % (clf, score...
SVCWrapper azureml.automl.runtime.shared.model_wrappers.SparseNormalizer azureml.automl.runtime.shared.model_wrappers.StandardScalerWrapper azureml.automl.runtime.shared.model_wrappers。TabnetClassifier azureml.automl.runtime.shared.model_wrappers。TabnetRegressor azureml.automl.runtime...
X_val = X_val[:,clf_fe.coef_.ravel()!=0] clf2_l = svm.SVC(kernel='linear', C=reg) clf2_l.fit(X_train, y_train)print"Lasso Validation set score filtered coeff linear: ", clf2_l.score(X_val, y_val) clf2 = svm.SVC(kernel='rbf', C=reg, gamma=g) ...
hp.uniform是一个内置的hyperopt函数,它有三个参数:名称x,范围的下限和上限0和1。 algo参数指定搜索算法,本例中tpe表示tree of Parzen estimators。该主题超出了本文的范围,但有数学背景的读者可以细读这篇[1]文章。algo参数也可以设置为hyperopt.random,但是这里我们没有涉及,因为它是众所周知的搜索策略。但在...
The experiments were performed using C-SVC version of the SVM classifier [49] with radial basis function kernel. The k-NN classifier [50] evaluates distances between a test data point and all training data points in a multidimensional feature space. Based on the calculated distances, the ...
#设置分类器,这里实用的是SVC支持向量机 clf =SVC(C=0.2,gamma=0.2,kernel='linear', probability=True, random_state=0) classifier = OneVsRestClassifier(clf,n_jobs=-1) classifier.fit(X_train, y_train) #计算分数,roc_curve要用到 y_score = classifier.decision_function(X_test) ...
X= iris.data[:,[0,2]]#取两列,方便绘图y =iris.target clf1= DecisionTreeClassifier(max_depth=4) clf2= KNeighborsClassifier(n_neighbors=7) clf3= SVC(kernel='rbf', probability=True) eclf= VotingClassifier(estimators=[('dt',clf1),('knn',clf2),('svc',clf3)], voting='soft',weights...