Predictability of Machine Learning Algorithms and Related Feature Extraction Techniques

Yunbo Dong
4/30/2023
cs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

This thesis designs a prediction system based on matrix factorization to predict the classification accuracy of a specific model on a particular dataset. In this thesis, we conduct comprehensive empirical research on more than fifty datasets that we collected from the openml website. We study the performance prediction of three fundamental machine learning algorithms, namely, random forest, XGBoost, and MultiLayer Perceptron(MLP). In particular, we obtain the following results: 1. Predictability of fine-tuned models using coarse-tuned variants. 2. Predictability of MLP using feature extraction techniques. 3. Predict model performance using implicit feedback.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks

82% match2025

Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study

82% match2024

Evaluation of Machine Learning Models in Student Academic Performance Prediction

81% match2025

Data Complexity-aware Deep Model Performance Forecasting

81% match2026

Simulating classification models to evaluate Predict-Then-Optimize methods

80% match2025

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning

80% match2024

Cite this paper

@article{dong2023predictability,
  title  = {Predictability of Machine Learning Algorithms and Related Feature Extraction Techniques},
  author = {Yunbo Dong},
  year   = {2023},
  eprint = {2305.00449},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.00449v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2305.00449.svg)](https://codeofpaper.com/paper/2305.00449)

Discussion