importsklearnimportshapfromsklearn.model_selectionimporttrain_test_split# print the JS visualization code to the notebookshap.initjs()# train a SVM classifierX_train,X_test,Y_train,Y_test=train_test_split(*shap.datasets.iris(),test_size=0.2,random_state=0)svm=sklearn.svm.SVC(kernel='rbf...
breast_cancer_data.target_names.tolist() # split data into train and test from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0) clf = svm.SVC(gamma...
['target']# train a SVM classifierX_train,X_test,y_train,y_test=train_test_split(df.drop(columns=["class"]),df["class"],test_size=0.2,random_state=0)svm=SVC(kernel='rbf',probability=True)svm.fit(X_train,y_train)predictions=svm.predict(X_train)# prepare model predictions to be ...
breast_cancer_data.target_names.tolist() # split data into train and test from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0) clf = svm.SVC(gamma...
( handle_unknown='ignore', sparse=False))forfincategorical] transformations = numeric_transformations + categorical_transformations# append model to preprocessing pipeline.# now we have a full prediction pipeline.clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)), ('classifier', ...
This model explained its decision in terms of words or phrases that describe its logic and may communicate directly with both expert and ordinary users [26]. A justification model that utilized inputs from the classifier’s visual characteristics, as well as prediction embeddings, was used to con...