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Found 10 papers, 1 papers with code
Towards understanding the bias in decision trees
In this study, we show that this belief is not necessarily correct for decision trees, and that their bias can actually be in the opposite direction.
Challenges learning from imbalanced data using tree-based models: Prevalence estimates systematically depend on hyperparameters and can be upwardly biased
One way of accounting for this bias is to analytically map the resulting predictions to new values based on the sampling rate for the majority class, which was used to create the training dataset.
Using Platt's scaling for calibration after undersampling -- limitations and how to address them
We show analytically, as well as via a simulation study and a case study, that Platt's scaling should not be used for calibration after undersampling without critical thought.
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation
Reinforcement learning (RL) has helped improve decision-making in several applications.
Hybrid Feature- and Similarity-Based Models for Joint Prediction and Interpretation
Electronic health records (EHRs) include simple features like patient age together with more complex data like care history that are informative but not easily represented as individual features.
Decision-Directed Data Decomposition
We present an algorithm, Decision-Directed Data Decomposition (D4), which decomposes a dataset into two components.
On Prediction and Tolerance Intervals for Dynamic Treatment Regimes
We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime.
gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity
However, in practice, problems can exhibit predictive heterogeneity: most instances might be relatively easy to predict, while others might be predictive outliers for which a model trained on the entire dataset does not perform well.
Generative Multiple-Instance Learning Models For Quantitative Electromyography
We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems.
Convergent Fitted Value Iteration with Linear Function Approximation
Fitted value iteration (FVI) with ordinary least squares regression is known to diverge.
