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 ...
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 ...
Classification (Fully Connected Layer) Different "filter" can produce different feature maps. Another good way to understand the Convolution operations is by looking at the animation below. The filter with red arrows produce one feature map and the filter with green arrows produce another feature ...
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. Image Created by Author. If the value ofkis too...
The success of learning with kernels (again, at least for SVMs), very strongly depends on the choice of kernel. You can see a kernel as a compact representation of the knowledge about your classification problem. It is very often problem specific. I would not call a kernel a decision funct...
Classification (Fully Connected Layer) Different "filter" can produce different feature maps. Another good way to understand the Convolution operations is by looking at the animation below. The filter with red arrows produce one feature map and the filter with green arrows produce another feature ...
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...
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. ...
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. import sklearn import shap from sklearn...
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. import sklearn import shap from sklearn...