In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be light. InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train inter...
Unfortunately, interpretable classification models, such as linear, rule-based, and decision tree models, are superseded by more accurate but complex learning paradigms, such as deep neural networks and ensemble methods. For tabular data classification, more specifically, models based on gradient-boosted...
While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods (see ourNature MI paper). Fast C++ implementations are supported forXGBoost,LightGBM,CatBoost,scikit-learnandpysparktree models: ...
Thus, predictive machine learning (ML) techniques could be crucial to aid clinicians in identifying high-risk BRCA -mutated patients and determining the appropriate timing for performing RRSO. Methods: In this work, we addressed this task by developing explainable ML models usi...
If you are interested in learning more about SHAP values, we recommend reading a chapter devoted to them in one of these books: Explanatory Model Analysis or Interpretable Machine Learning. The main advantage of SHAP values in contrast to other methods is a solid theory standing behind them. We...
Methods The objective of this work is to identify and quantify the effect that various socio-economic variables have in determining the risk class associated with each European region. To achieve this goal we opted for a classification-based machine learning approach, using models that provide some...
When land cover change was detected at least once in a pixel, it was classified as a disturbed area. The land cover classification was accomplished through a machine-learning method, a random forest (RF) algorithm. The RF classifier is an ensemble classifier that uses a set of classification ...
in obtaining transparency in the case of machine learning (ML) applied to classification of retina conditions. High performance metrics achieved using ML has become common practice. However, in the medical domain, algorithmic decisions need to be sustained by explanations. We aim at building a ...
True colour vision requires comparing the responses of different spectral classes of photoreceptors. In insects, there is a wealth of data available on the physiology of photoreceptors and on colour-dependent behaviour, but less is known about the neural
Ensemble-based and single machine-learning classifiers were utilized; accuracies were evaluated using a confusion-matrix and area under the curve (AUC) of a sensitivityspecificity plot. Results: We evaluated 42 COPD patients with three year follow-up data, 27 of whom (9 Females/18 Males, 66...