Model-independent variable selection via the rule-based variable priority
- URL: http://arxiv.org/abs/2409.09003v3
- Date: Tue, 1 Oct 2024 12:42:24 GMT
- Title: Model-independent variable selection via the rule-based variable priority
- Authors: Min Lu, Hemant Ishwaran,
- Abstract summary: We introduce a new model-independent approach, Variable Priority (VarPro)
VarPro works by utilizing rules without the need to generate artificial data or evaluate prediction error.
We show that VarPro has a consistent filtering property for noise variables.
- Score: 1.2771542695459488
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While achieving high prediction accuracy is a fundamental goal in machine learning, an equally important task is finding a small number of features with high explanatory power. One popular selection technique is permutation importance, which assesses a variable's impact by measuring the change in prediction error after permuting the variable. However, this can be problematic due to the need to create artificial data, a problem shared by other methods as well. Another problem is that variable selection methods can be limited by being model-specific. We introduce a new model-independent approach, Variable Priority (VarPro), which works by utilizing rules without the need to generate artificial data or evaluate prediction error. The method is relatively easy to use, requiring only the calculation of sample averages of simple statistics, and can be applied to many data settings, including regression, classification, and survival. We investigate the asymptotic properties of VarPro and show, among other things, that VarPro has a consistent filtering property for noise variables. Empirical studies using synthetic and real-world data show the method achieves a balanced performance and compares favorably to many state-of-the-art procedures currently used for variable selection.
Related papers
- Fractional Naive Bayes (FNB): non-convex optimization for a parsimonious weighted selective naive Bayes classifier [0.0]
We supervised classification for datasets with a very large number of input variables.
We propose a regularization of the model log-like Baylihood.
The various proposed algorithms result in optimization-based weighted na"ivees scheme.
arXiv Detail & Related papers (2024-09-17T11:54:14Z) - Conformalization of Sparse Generalized Linear Models [2.1485350418225244]
Conformal prediction method estimates a confidence set for $y_n+1$ that is valid for any finite sample size.
Although attractive, computing such a set is computationally infeasible in most regression problems.
We show how our path-following algorithm accurately approximates conformal prediction sets.
arXiv Detail & Related papers (2023-07-11T08:36:12Z) - Adaptive Selection of the Optimal Strategy to Improve Precision and
Power in Randomized Trials [2.048226951354646]
We show how to select the adjustment approach -- which variables and in which form -- to maximize precision.
Our approach maintains Type-I error control (under the null) and offers substantial gains in precision.
When applied to real data, we also see meaningful efficiency improvements overall and within subgroups.
arXiv Detail & Related papers (2022-10-31T16:25:38Z) - Predicting Out-of-Domain Generalization with Neighborhood Invariance [59.05399533508682]
We propose a measure of a classifier's output invariance in a local transformation neighborhood.
Our measure is simple to calculate, does not depend on the test point's true label, and can be applied even in out-of-domain (OOD) settings.
In experiments on benchmarks in image classification, sentiment analysis, and natural language inference, we demonstrate a strong and robust correlation between our measure and actual OOD generalization.
arXiv Detail & Related papers (2022-07-05T14:55:16Z) - Determination of class-specific variables in nonparametric
multiple-class classification [0.0]
We propose a probability-based nonparametric multiple-class classification method, and integrate it with the ability of identifying high impact variables for individual class.
We report the properties of the proposed method, and use both synthesized and real data sets to illustrate its properties under different classification situations.
arXiv Detail & Related papers (2022-05-07T10:08:58Z) - Equivariance Allows Handling Multiple Nuisance Variables When Analyzing
Pooled Neuroimaging Datasets [53.34152466646884]
In this paper, we show how bringing recent results on equivariant representation learning instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution.
We demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples.
arXiv Detail & Related papers (2022-03-29T04:54:06Z) - A Lagrangian Duality Approach to Active Learning [119.36233726867992]
We consider the batch active learning problem, where only a subset of the training data is labeled.
We formulate the learning problem using constrained optimization, where each constraint bounds the performance of the model on labeled samples.
We show, via numerical experiments, that our proposed approach performs similarly to or better than state-of-the-art active learning methods.
arXiv Detail & Related papers (2022-02-08T19:18:49Z) - Selecting the suitable resampling strategy for imbalanced data
classification regarding dataset properties [62.997667081978825]
In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class.
This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples.
Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
arXiv Detail & Related papers (2021-12-15T18:56:39Z) - Model-agnostic and Scalable Counterfactual Explanations via
Reinforcement Learning [0.5729426778193398]
We propose a deep reinforcement learning approach that transforms the optimization procedure into an end-to-end learnable process.
Our experiments on real-world data show that our method is model-agnostic, relying only on feedback from model predictions.
arXiv Detail & Related papers (2021-06-04T16:54:36Z) - Flexible Model Aggregation for Quantile Regression [92.63075261170302]
Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions.
We investigate methods for aggregating any number of conditional quantile models.
All of the models we consider in this paper can be fit using modern deep learning toolkits.
arXiv Detail & Related papers (2021-02-26T23:21:16Z) - Evaluating Prediction-Time Batch Normalization for Robustness under
Covariate Shift [81.74795324629712]
We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift.
We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness.
The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
arXiv Detail & Related papers (2020-06-19T05:08:43Z)
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.