svm.SVC(kernel='rbf', probability=True) svm.fit(X_train, Y_train) # use Kernel SHAP to explain test set predictions explainer = shap.KernelExplainer(svm.predict_proba, X_train, link="logit") shap_values = explainer.shap_values(X_test, nsamples=100) # plot the SHAP values for the ...
svm.SVC(kernel='rbf', probability=True) svm.fit(X_train, Y_train) # use Kernel SHAP to explain test set predictions explainer = shap.KernelExplainer(svm.predict_proba, X_train, link="logit") shap_values = explainer.shap_values(X_test, nsamples=100) # plot the SHAP values for the ...
kernel_function=chi2_kernelifnot(self.model_kernel=='linear'orself.model_kernel=='rbf')elseself.model_kernel self.model=SVC(C=1,kernel=kernel_function,gamma=1,probability=True)elifmodel_name=='lr': self.model=LR(C=1,penalty='l1',tol=1e-6)else:ifmodel_name=='xgb': self.model=XGBCl...
SVC(kernel=params['kernel'], C=params['C'], gamma=params['gamma'], probability=True, random_state=self.random_state) clf.fit(X_train, y_train) return clf Example #5Source File: test_search.py From Mastering-Elasticsearch-7.0 with MIT License 6 votes def test_random_search_bad_cv()...
clf=svm.SVC(kernel=params['kernel'], C=params['C'], gamma=params['gamma'], probability=True, random_state=self.random_state) clf.fit(X_train, y_train)returnclf 开发者ID:daniellerch,项目名称:aletheia,代码行数:25,代码来源:models.py ...
('testY_feat', testY) #fit the svm model and compute accuaracy measure #clf = svm.SVC(kernel=kernel.arc_cosine, cache_size=2048) #regr = SVR(kernel='rbf', C=1e3, gamma=0.1) regr = GridSearchCV(SVR(kernel='rbf', gamma=0.1), cv=5, n_jobs=-1, param_grid...
生成kernel 矩阵,用于将 svm kernel 的数据标准化 classpreprocessing.KernelCenterer: 1. 以上几个标准化类的方法: fit(X[,y]):根据数据 X 的值,设置标准化缩放的比例 transform(X[,y, copy]):用之前设置的比例标准化 X fit_transform(X[, y]):根据 X设置标准化缩放比例并标准化 partial_fit(X[,y]...
'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc']>>>clf=svm.SVC(probability=True,random_state=0)>>>cross_validation.cross_val_score(clf,X,y,scoring='log_loss')array([-0.07..., -0.16...,...
numpy exploratory-data-analysis machine-learning-algorithms prediction pandas data-visualization seaborn lightgbm matplotlib data-preprocessing decision-tree-classifier scikitlearn-machine-learning random-forest-classifier xgboost-model knn-classifier svc-model catboost-model logistic-regression-classifier mlxtend pcos...
(kernel='rbf',probability=True)svm.fit(X_train,Y_train)# use Kernel SHAP to explain test set predictionsexplainer=shap.KernelExplainer(svm.predict_proba,X_train,link="logit")shap_values=explainer.shap_values(X_test,nsamples=100)# plot the SHAP values for the Setosa output of the first ...