Deep learning example with DeepExplainer (TensorFlow/Keras models) Deep SHAP is a high-speed approximation algorithm for SHAP values in deep learning models that builds on a connection withDeepLIFTdescribed in the SHAP NIPS paper. The implementation here differs from the original DeepLIFT by using a...
Deep learning example with DeepExplainer (TensorFlow/Keras models) Deep SHAP is a high-speed approximation algorithm for SHAP values in deep learning models that builds on a connection withDeepLIFTdescribed in the SHAP NIPS paper. The implementation here differs from the original DeepLIFT by using a...
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) ...
The Baum-Welch algorithm only guarantees reaching a local rather than global maximum of the likelihood. Hence, for each session, after selecting the number of pattern M∗ as above, we ran 20 independent HMM fits on the whole session, with random initial guesses for emission and transition pro...
For example, if we created one decision tree, the third one, it would predict 0. But if we relied on the mode of all 4 decision trees, then the predicted value would be 1. This is the power of random forests. AdaBoost AdaBoost is a boosted algorithm that is similar to Random Forest...
Deep learning example with DeepExplainer (TensorFlow/Keras models) Deep SHAP is a high-speed approximation algorithm for SHAP values in deep learning models that builds on a connection with DeepLIFT described in the SHAP NIPS paper. The implementation here differs from the original DeepLIFT by using...
Tree ensemble example (XGBoost/LightGBM/CatBoost/scikit-learn models) While SHAP values can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods (Tree SHAP arXiv paper). Fast C++ implementations are supported for XGBoost, LightGB...
Deep learning example with DeepExplainer (TensorFlow/Keras models) Deep SHAP is a high-speed approximation algorithm for SHAP values in deep learning models that builds on a connection with DeepLIFT described in the SHAP NIPS paper. The implementation here differs from the original DeepLIFT by using...
Deep learning example with DeepExplainer (TensorFlow/Keras models) Deep SHAP is a high-speed approximation algorithm for SHAP values in deep learning models that builds on a connection withDeepLIFTdescribed in the SHAP NIPS paper. The implementation here differs from the original DeepLIFT by using a...
Tree ensemble example (XGBoost/LightGBM/CatBoost/scikit-learn models) While SHAP values can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods (Tree SHAP arXiv paper). Fast C++ implementations are supported for XGBoost, LightGB...