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...
For guidance on how to enable interpretability for models trained with automated machine learning see, Interpretability: model explanations for automated machine learning models (preview).Generate feature importance value on your personal machineThe following example shows how to use the interpretability ...
svm import SVC import pandas as pd from rulexai.explainer import Explainer # load iris dataset data = load_iris() df = pd.DataFrame(data['data'], columns=data['feature_names']) df['class'] = data['target'] # train a SVM classifier X_train,X_test,y_train,y_test = train_test_...
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(gamm...
( 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...