The proposed framework outperformed predicted probabilities and entropy-based methods of identifying instances at risk of being misclassified. Furthermore, the proposed approach resulted in uncertainty estimates
Introducing the Explainable Boosting Machine (EBM) EBM is an interpretable model developed at Microsoft Research*. It uses modern machine learning techniques like bagging, gradient boosting, and automatic interaction detection to breathe new life into traditional GAMs (Generalized Additive Models). This ...
Comparing to a generic two-state model, our models can distinguish class I from class II MHCs and correlate their structural parameters with the TCR/pMHC’s potency to trigger T cell activation. The models are tested by mutagenesis using an MHC and a TCR mutated to alter conformation changes....
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: ...
That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we...
Discuss the pros and cons of k-means clustering compared to hierarchical clustering. What is the difference between classification and regression? What is a classification algorithm? What is unsupervised classification? What is rule-based classification?
Excess cash is predicted by the model estimated cross-sectionally each year at the two exchanges. (Data source: CSMAR) Supervisory Letter An indicator variable equals one if the firm receives a supervisory letter related to corporate governance issues, and zero otherwise. (Data source: CSMAR) ...
That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we...
Therefore, different aspects of snow cover can be useful in refining model-based predictions of future treeline change. Although climate is certainly a driver of change in treelines around the world, the spatiotemporal dynamics of snow cover play a role in tree growth and seedling establishment ...
We overlaid a disease-spread model on the mobility network, with each CBG having its own set of SEIR compartments. New infections occur at both POIs and CBGs, with the mobility network governing how subpopulations from different CBGs interact as they visit POIs.c, Left, to test the out-of...