Summary of Unmasking Trees For Tabular Data, by Calvin Mccarter
Unmasking Trees for Tabular Data
by Calvin McCarter
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary UnmaskingTrees is a simple method for tabular imputation (and generation) that employs gradient-boosted decision trees to incrementally unmask individual features. This approach offers state-of-the-art performance on imputation and competitive performance on vanilla generation when given training data with missingness. The paper also proposes BaltoBot, a tabular probabilistic prediction method that fits a balanced tree of boosted tree classifiers without requiring parametric assumptions on the conditional distribution. Unlike older methods, BaltoBot accommodates features with multimodal distributions; unlike newer diffusion methods, it offers fast sampling, closed-form density estimation, and flexible handling of discrete variables. The paper considers UnmaskingTrees and BaltoBot as meta-algorithms, demonstrating in-context learning-based generative modeling with TabPFN. Keywords: tabular imputation, generation, gradient-boosted decision trees, BaltoBot, TabPFN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to fill in missing data (imputation) and generate new data that looks like real data. The method uses special kinds of tree-like models called decision trees to figure out which parts of the data are missing and then fills them in. This approach works really well for imputing missing data and is competitive with other methods for generating new data. The paper also proposes a new way to predict the probability of certain features having certain values, without making assumptions about how those values are distributed. This approach can handle complex distributions and is fast and efficient. |
Keywords
» Artificial intelligence » Density estimation » Diffusion » Probability
