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Jingyi Jessica Li | Statistical Hypothesis Testing vs Machine Learning Binary Classification

Jingyi Jessica Li | Statistical Hypothesis Testing vs Machine Learning Binary Classification

FromData & Science with Glen Wright Colopy


Jingyi Jessica Li | Statistical Hypothesis Testing vs Machine Learning Binary Classification

FromData & Science with Glen Wright Colopy

ratings:
Length:
56 minutes
Released:
Sep 19, 2021
Format:
Podcast episode

Description

Jingyi Jessica Li | Statistical Hypothesis Testing versus Machine Learning Binary Classification
Jingyi Jessica Li  (UCLA) discusses her paper "Statistical Hypothesis Testing versus Machine Learning Binary Classification". Jingyi noticed several high-impact cancer research papers using multiple hypothesis testing for binary classification problems. Concerned that these papers had no guarantee on their claimed false discovery rates, Jingyi wrote a perspective article about clarifying hypothesis testing and binary classification to scientists.
#datascience #science #statistics
0:00 – Intro
1:50 – Motivation for Jingyi's article
3:22 – Jingyi's four concepts under hypothesis testing and binary
classification
8:15 – Restatement of concepts
12:25 – Emulating methods from other publications
13:10 – Classification vs hypothesis test: features vs instances
21:55 - Single vs multiple instances
23:55 - Correlations vs causation
24:30 - Jingyi’s Second and Third Guidelines
30:35 - Jingyi’s Fourth Guideline
36:15 - Jingyi’s Fifth Guideline
39:15 – Logistic regression: An inference method & a classification method
42:15 – Utility for students
44:25 – Navigating the multiple comparisons problem (again!)
51:25 – Right side, show bio-arxiv paper
Released:
Sep 19, 2021
Format:
Podcast episode

Titles in the series (88)

Data and Science with Glen Wright Colopy is a podcast covering critical scientific reasoning, particularly from a data science / machine learning / statistics perspective. Episodes typically focus on understanding of how to be better scientists and critical thinkers for the practical purpose of being a better data scientists. Previously called: ”Pod of Asclepius”