Model agnostic example with KernelExplainer (explains any function) Kernel SHAP uses a specially-weighted local linear regression to estimate SHAP values for any model. Below is a simple example for explaining a
As an upgrade, we have eliminated the need to pass in the model name as explainX is smart enough to identify the model type and problem type i.e. classification or regression, by itself. You can access multiple modules: Module 1: Dataframe with Predictions ...
Metawa, Hassan, and Elhoseny (2017) use an intelligent model based on a genetic algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint. Abedin et al. (2019) use 12 feature selection methods for support vector machine (SVM) ...
clear;clc;close all imds = imageDatastore('Concrete Crack Images for Classification','IncludeSubfolders',true, 'LabelSource','foldernames'); This dataset contains normal (Negative) and crack images (Positive). Display some sample images. numExample=16; idx = randperm(numel(imds.Files)...
K-Nearest Neighbours is a classification technique where a new sample is classified by looking at the nearest classified points, hence ‘K-nearest.’ In the example below, ifk=1, then an unclassified point would be classified as a blue point. ...
Kernel SHAP uses a specially-weighted local linear regression to estimate SHAP values for any model. Below is a simple example for explaining a multi-class SVM on the classic iris dataset. importsklearnimportshapfromsklearn.model_selectionimporttrain_test_split# print the JS visualization code to...
["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 fed to RuleXAI, remember to change numerical predictions to labels (in this example it is simply converting predictions to...
Below is a simple example for explaining a multi-class SVM on the classic iris dataset. import sklearn import shap from sklearn.model_selection import train_test_split # print the JS visualization code to the notebook shap.initjs() # train a SVM classifier X_train,X_test,Y_train,Y_...
Below is a simple example for explaining a multi-class SVM on the classic iris dataset. import sklearn import shap from sklearn.model_selection import train_test_split # print the JS visualization code to the notebook shap.initjs() # train a SVM classifier X_train,X_test,Y_train,Y_...
Model agnostic example with KernelExplainer (explains any function) Kernel SHAP uses a specially-weighted local linear regression to estimate SHAP values for any model. Below is a simple example for explaining a multi-class SVM on the classic iris dataset. ...