Usage (Example) After successfully installing explainX, open up your Python IDE of Jupyter Notebook and simply follow the code below to use it: Import required module. fromexplainximport*fromsklearn.ensembleimportRandomForestClassifierfromsklearn.model_selectionimporttrain_test_split ...
Dataset/AUROCDomainLogistic RegressionRandom ForestXGBoostExplainable Boosting Machine Adult IncomeFinance.907±.003.903±.002.927±.001.928±.002 Heart DiseaseMedical.895±.030.890±.008.851±.018.898±.013 Breast CancerMedical.995±.005.992±.009.992±.010.995±.006 ...
Seems like the second model, the random forest performed the best (highest mean accuracy with lowest standard error). So let’s retrain the model on the whole training set and see how it fares on the testing set: rf_specs <- trained_models_list[[2]] Let’s save the best model specifi...
The package is not yet fully developed but it can already compute explanations for a range of models including XGBoost, LightGBM, gbm, ranger and randomForest,(catboost in the plans for the nearest future) and present the results with various plotting functions. Recently we added an option to ...
Random Forest is an ensemble technique, meaning that it combines several models into one to improve its predictive power. Specifically, it builds 1000s of smaller decision trees using bootstrapped datasets and random subsets of variables (also known as bagging). With 1000s of smaller decision trees...
However, research on the interpretability of ML models such as Random Forest (RF) is still in the infant stage. Considering the strict and standardized characteristics of formal methods and their wide application in the field of ML in recent years, this study lever...
Forest species with low specific root length and high root tissue density (RTD) were more likely to occur in warm climates but species with high specific root length and low RTD were more likely to occur in cold climates. Unidirectional benefits were more prevalent than trade-offs: for example...
Nuclear power, for example, was originally billed as a virtually free, everlasting source of energy, but it was developed at enormous expense, and with huge amounts of highly toxic nuclear waste as a byproduct, largely so the world's superpowers could develop nuclear weapons at the same ...
It started when a TikTok account asked seven women: If you're alone in the forest would you rather encounter a bear or a man? Six out of the seven women picked the bear over the male stranger, a majority answer that only solidified as more and more women started responding. The ...
Deep learning example with GradientExplainer (TensorFlow/Keras/PyTorch models) Expected gradients combines ideas from Integrated Gradients, SHAP, and SmoothGrad into a single expected value equation. This allows an entire dataset to be used as the background distribution (as opposed to a single referen...