High Dimensional Human Guided Machine Learning

TL;DR

This paper compares human-engineered features with raw data for training XGBoost models, introducing a new method to utilize human-created classifiers on high-dimensional datasets, showing comparable performance.

Contribution

It presents a novel approach for leveraging human-designed classification models in high-dimensional machine learning tasks.

Findings

Human-engineered features are comparable to raw data for XGBoost training.

The proposed method effectively utilizes human-created classifiers on complex datasets.

No significant outperforming of raw data over engineered features was observed.

Abstract

Have you ever looked at a machine learning classification model and thought, I could have made that? Well, that is what we test in this project, comparing XGBoost trained on human engineered features to training directly on data. The human engineered features do not outperform XGBoost trained di- rectly on the data, but they are comparable. This project con- tributes a novel method for utilizing human created classifi- cation models on high dimensional datasets.