Hidden Variables unseen by Random Forests
- URL: http://arxiv.org/abs/2406.15500v1
- Date: Wed, 19 Jun 2024 12:07:22 GMT
- Title: Hidden Variables unseen by Random Forests
- Authors: Ricardo Blum, Munir Hiabu, Enno Mammen, Joseph Theo Meyer,
- Abstract summary: We argue that simple alternative partitioning schemes used in the tree growing procedure can enhance identification of these interactions.
Our results validate that the modifications considered enhance the model's fitting ability in scenarios where pure interactions play a crucial role.
- Score: 0.3749861135832073
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Random Forests are widely claimed to capture interactions well. However, some simple examples suggest that they perform poorly in the presence of certain pure interactions that the conventional CART criterion struggles to capture during tree construction. We argue that simple alternative partitioning schemes used in the tree growing procedure can enhance identification of these interactions. In a simulation study we compare these variants to conventional Random Forests and Extremely Randomized trees. Our results validate that the modifications considered enhance the model's fitting ability in scenarios where pure interactions play a crucial role.
Related papers
- Ensembles of Probabilistic Regression Trees [46.53457774230618]
Tree-based ensemble methods have been successfully used for regression problems in many applications and research studies.
We study ensemble versions of probabilisticregression trees that provide smooth approximations of the objective function by assigningeach observation to each region with respect to a probability distribution.
arXiv Detail & Related papers (2024-06-20T06:51:51Z) - Why do Random Forests Work? Understanding Tree Ensembles as
Self-Regularizing Adaptive Smoothers [68.76846801719095]
We argue that the current high-level dichotomy into bias- and variance-reduction prevalent in statistics is insufficient to understand tree ensembles.
We show that forests can improve upon trees by three distinct mechanisms that are usually implicitly entangled.
arXiv Detail & Related papers (2024-02-02T15:36:43Z) - On the Pointwise Behavior of Recursive Partitioning and Its Implications
for Heterogeneous Causal Effect Estimation [8.394633341978007]
Decision tree learning is increasingly being used for pointwise inference.
We show that adaptive decision trees can fail to achieve convergence rates of convergence in the norm with non-vanishing probability.
We show that random forests can remedy the situation, turning poor performing trees into nearly optimal procedures.
arXiv Detail & Related papers (2022-11-19T21:28:30Z) - Contextual Decision Trees [62.997667081978825]
We propose a multi-armed contextual bandit recommendation framework for feature-based selection of a single shallow tree of the learned ensemble.
The trained system, which works on top of the Random Forest, dynamically identifies a base predictor that is responsible for providing the final output.
arXiv Detail & Related papers (2022-07-13T17:05:08Z) - What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work? [1.1050303097572156]
We show that both methods can be understood in terms of the same parameters and confounding assumptions under L2 loss.
In the randomized setting, both approaches performed akin to the new blended versions in a benchmark study.
arXiv Detail & Related papers (2022-06-21T12:45:07Z) - On Uncertainty Estimation by Tree-based Surrogate Models in Sequential
Model-based Optimization [13.52611859628841]
We revisit various ensembles of randomized trees to investigate their behavior in the perspective of prediction uncertainty estimation.
We propose a new way of constructing an ensemble of randomized trees, referred to as BwO forest, where bagging with oversampling is employed to construct bootstrapped samples.
Experimental results demonstrate the validity and good performance of BwO forest over existing tree-based models in various circumstances.
arXiv Detail & Related papers (2022-02-22T04:50:37Z) - Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a
Random Forest [8.513154770491898]
We argue that tree depth should be seen as a natural form of regularization across the entire procedure.
In particular, our work suggests that random forests with shallow trees are advantageous when the signal-to-noise ratio in the data is low.
arXiv Detail & Related papers (2021-03-30T21:57:55Z) - Composed Variational Natural Language Generation for Few-shot Intents [118.37774762596123]
We generate training examples for few-shot intents in the realistic imbalanced scenario.
To evaluate the quality of the generated utterances, experiments are conducted on the generalized few-shot intent detection task.
Our proposed model achieves state-of-the-art performances on two real-world intent detection datasets.
arXiv Detail & Related papers (2020-09-21T17:48:43Z) - Learning Disentangled Representations with Latent Variation
Predictability [102.4163768995288]
This paper defines the variation predictability of latent disentangled representations.
Within an adversarial generation process, we encourage variation predictability by maximizing the mutual information between latent variations and corresponding image pairs.
We develop an evaluation metric that does not rely on the ground-truth generative factors to measure the disentanglement of latent representations.
arXiv Detail & Related papers (2020-07-25T08:54:26Z) - Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies [76.83991682238666]
Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs)
We propose a novel imitation learning framework, and introduce new input features and architectures to represent branching.
arXiv Detail & Related papers (2020-02-12T17:43:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.