ASGE Annual Postgraduate Course: Clinical Challenges and Real-Time Decision Making in GI Endoscopy (Live and Virtual) | May 2023-How is Machine Learning different from Statistics? The thought process

How is Machine Learning different from Statistics? The thought process

Video Transcription

Video Summary

In this video, Shravanthi discusses the differences between machine learning and statistics. She emphasizes the importance of understanding how machine learning algorithms reach certain inferences and how they can be applied in clinical practice. Shravanthi explains that machine learning models are designed to make accurate predictions, while statistical models are designed for inference and analyzing the relationships between variables. Machine learning algorithms handle high-dimensional datasets and treat the algorithm as a black box, while statistics use low-dimensional datasets with planned and careful selection of variables and models. Shravanthi also touches on the challenges of developing machine learning algorithms, such as insufficient training data, noisy data, irrelevant features, overfitting or underfitting of data, and the need for hyperparameter tuning and model selection. She concludes by discussing the importance of explainability, trustworthiness, and transparency in machine learning algorithms, and the future potential for user-friendly explainable interfaces.

Asset Subtitle

Sravanthi Parasa, MD

Keywords

machine learning

statistics

clinical practice

predictive models

explainability