Imputation for prediction: beware of diminishing returns

• URL: http://arxiv.org/abs/2407.19804v1
• Date: Mon, 29 Jul 2024 09:01:06 GMT
• Title: Imputation for prediction: beware of diminishing returns
• Authors: Marine Le Morvan, Gaël Varoquaux,
• Abstract summary: Missing values are prevalent across various fields, posing challenges for training and deploying predictive models. Recent theoretical and empirical studies indicate that simple constant imputation can be consistent and competitive. This study aims at clarifying if and when investing in advanced imputation methods yields significantly better predictions.
• Score: 12.424671213282256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Missing values are prevalent across various fields, posing challenges for training and deploying predictive models. In this context, imputation is a common practice, driven by the hope that accurate imputations will enhance predictions. However, recent theoretical and empirical studies indicate that simple constant imputation can be consistent and competitive. This empirical study aims at clarifying if and when investing in advanced imputation methods yields significantly better predictions. Relating imputation and predictive accuracies across combinations of imputation and predictive models on 20 datasets, we show that imputation accuracy matters less i) when using expressive models, ii) when incorporating missingness indicators as complementary inputs, iii) matters much more for generated linear outcomes than for real-data outcomes. Interestingly, we also show that the use of the missingness indicator is beneficial to the prediction performance, even in MCAR scenarios. Overall, on real-data with powerful models, improving imputation only has a minor effect on prediction performance. Thus, investing in better imputations for improved predictions often offers limited benefits.

Related papers

• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)
• Predicting from Predictions [18.393971232725015]
  We study how causal effects of predictions on outcomes can be identified from observational data. We show that supervised learning that predict from predictions can find transferable functional relationships between features, predictions, and outcomes.
  arXiv  Detail & Related papers  (2022-08-15T16:57:02Z)
• Pathologies of Pre-trained Language Models in Few-shot Fine-tuning [50.3686606679048]
  We show that pre-trained language models with few examples show strong prediction bias across labels. Although few-shot fine-tuning can mitigate the prediction bias, our analysis shows models gain performance improvement by capturing non-task-related features. These observations alert that pursuing model performance with fewer examples may incur pathological prediction behavior.
  arXiv  Detail & Related papers  (2022-04-17T15:55:18Z)
• Benchmarking missing-values approaches for predictive models on health databases [47.187609203210705]
  We conduct a benchmark of missing-values strategies in predictive models with a focus on large health databases. We find that native support for missing values in supervised machine learning predicts better than state-of-the-art imputation with much less computational cost.
  arXiv  Detail & Related papers  (2022-02-17T09:40:04Z)
• On the Relation between Prediction and Imputation Accuracy under Missing Covariates [0.0]
  Recent research has realized an increasing trend towards the usage of modern Machine Learning algorithms for imputation. Recent research has realized an increasing trend towards the usage of modern Machine Learning algorithms for imputation.
  arXiv  Detail & Related papers  (2021-12-09T23:30:44Z)
• Learning to Predict Trustworthiness with Steep Slope Loss [69.40817968905495]
  We study the problem of predicting trustworthiness on real-world large-scale datasets. We observe that the trustworthiness predictors trained with prior-art loss functions are prone to view both correct predictions and incorrect predictions to be trustworthy. We propose a novel steep slope loss to separate the features w.r.t. correct predictions from the ones w.r.t. incorrect predictions by two slide-like curves that oppose each other.
  arXiv  Detail & Related papers  (2021-09-30T19:19:09Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Loss Estimators Improve Model Generalization [36.520569284970456]
  We propose to train a loss estimator alongside the predictive model, using a contrastive training objective, to directly estimate the prediction uncertainties. We show the impact of loss estimators on model generalization, in terms of both its fidelity on in-distribution data and its ability to detect out of distribution samples or new classes unseen during training.
  arXiv  Detail & Related papers  (2021-03-05T16:35:10Z)
• Learning Prediction Intervals for Model Performance [1.433758865948252]
  We propose a method to compute prediction intervals for model performance. We evaluate our approach across a wide range of drift conditions and show substantial improvement over competitive baselines.
  arXiv  Detail & Related papers  (2020-12-15T21:32:03Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)

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.