Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark

• URL: http://arxiv.org/abs/2102.01130v1
• Date: Mon, 1 Feb 2021 19:45:47 GMT
• Title: Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
• Authors: Shenhao Wang, Baichuan Mo, Stephane Hess, Jinhua Zhao
• Abstract summary: This study seeks to provide a generalizable empirical benchmark by comparing hundreds of machine learning (ML) and discrete choice models (DCMs) Experiments evaluate both prediction accuracy and computational cost by spanning four hyper-dimensions. Deep neural networks achieve the highest predictive performance, but at a relatively high computational cost.
• Score: 3.0969191504482247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Researchers have compared machine learning (ML) classifiers and discrete choice models (DCMs) in predicting travel behavior, but the generalizability of the findings is limited by the specifics of data, contexts, and authors' expertise. This study seeks to provide a generalizable empirical benchmark by comparing hundreds of ML and DCM classifiers in a highly structured manner. The experiments evaluate both prediction accuracy and computational cost by spanning four hyper-dimensions, including 105 ML and DCM classifiers from 12 model families, 3 datasets, 3 sample sizes, and 3 outputs. This experimental design leads to an immense number of 6,970 experiments, which are corroborated with a meta dataset of 136 experiment points from 35 previous studies. This study is hitherto the most comprehensive and almost exhaustive comparison of the classifiers for travel behavioral prediction. We found that the ensemble methods and deep neural networks achieve the highest predictive performance, but at a relatively high computational cost. Random forests are the most computationally efficient, balancing between prediction and computation. While discrete choice models offer accuracy with only 3-4 percentage points lower than the top ML classifiers, they have much longer computational time and become computationally impossible with large sample size, high input dimensions, or simulation-based estimation. The relative ranking of the ML and DCM classifiers is highly stable, while the absolute values of the prediction accuracy and computational time have large variations. Overall, this paper suggests using deep neural networks, model ensembles, and random forests as baseline models for future travel behavior prediction. For choice modeling, the DCM community should switch more attention from fitting models to improving computational efficiency, so that the DCMs can be widely adopted in the big data context.

Related papers

• Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference [55.150117654242706]
  We show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty.
  arXiv  Detail & Related papers  (2024-11-01T21:11:48Z)
• A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics [4.220363193932374]
  We propose an efficient cosine similarity-based classification difficulty measure S. It is calculated from the number of classes and intra- and inter-class similarity metrics of the dataset. We show how a practitioner can use this measure to help select an efficient model 6 to 29x faster than through repeated training and testing.
  arXiv  Detail & Related papers  (2024-04-09T03:27:09Z)
• No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance [68.18779562801762]
  multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance. Our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training paradigms remains to be found.
  arXiv  Detail & Related papers  (2024-04-04T17:58:02Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Activity Cliff Prediction: Dataset and Benchmark [20.41770222873952]
  We first introduce ACNet, a large-scale dataset for AC prediction. ACNet curates over 400K Matched Molecular Pairs (MMPs) against 190 targets. We propose a baseline framework to benchmark the predictive performance of molecular representations encoded by deep neural networks for AC prediction.
  arXiv  Detail & Related papers  (2023-02-15T09:19:07Z)
• Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data [0.0]
  This research develops an approximate Gibbs sampler (AGS) to efficiently learn the HBPRMs while maintaining the inference accuracy. Numerical experiments using real and synthetic datasets with small and large counts demonstrate the superior performance of AGS.
  arXiv  Detail & Related papers  (2022-11-28T21:00:55Z)
• Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study [3.7881729884531805]
  The paper is organized in a findings-based manner, with each section providing general conclusions. Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models. RF did not perform well in general, confirming the findings in the literature.
  arXiv  Detail & Related papers  (2022-04-27T12:04:33Z)
• Sparse MoEs meet Efficient Ensembles [49.313497379189315]
  We study the interplay of two popular classes of such models: ensembles of neural networks and sparse mixture of experts (sparse MoEs) We present Efficient Ensemble of Experts (E$3$), a scalable and simple ensemble of sparse MoEs that takes the best of both classes of models, while using up to 45% fewer FLOPs than a deep ensemble.
  arXiv  Detail & Related papers  (2021-10-07T11:58:35Z)
• Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays [62.997667081978825]
  Generalized Linear Latent Variable models (GLLVMs) generalize such factor models to non-Gaussian responses. Current algorithms for estimating model parameters in GLLVMs require intensive computation and do not scale to large datasets. We propose a new approach for fitting GLLVMs to high-dimensional datasets, based on approximating the model using penalized quasi-likelihood.
  arXiv  Detail & Related papers  (2020-10-06T04:28:19Z)
• Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation [97.42894942391575]
  We propose FAST-DAD to distill arbitrarily complex ensemble predictors into individual models like boosted trees, random forests, and deep networks. Our individual distilled models are over 10x faster and more accurate than ensemble predictors produced by AutoML tools like H2O/AutoSklearn.
  arXiv  Detail & Related papers  (2020-06-25T09:57:47Z)

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.