Practical AI: Machine Learning, Data Science, LLM

When data leakage turns into a flood of trouble

Oct 20, 2020

48:27

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Episode notes

Introduction

3min

The Risks of Using a Classification Model

2min

How to Leak Information From Your Training and Datasets

3min

The Importance of Social Grounding in AI

2min

The Implications of Data Leakage

3min

How to Avoid Target Leakage in Data Science

5min

The Effects of Aftershocks on Model Learning

3min

How to Fix Data Leakage

2min

How to Avoid Target Leakage in Data Science

4min

10.

How to Monitor Your Model for Leakage

3min

11.

How to Document Your Process on Projects

4min

12.

The Importance of Understanding Your Model

3min

13.

The Practicalities of Machine Learning and AI

2min

14.

How to Be a Successful Data Scientist

3min

15.

How to Integrate a Model Into Other Things

4min

16.

The Practical Side of AI

2min

Rajiv Shah teaches Daniel and Chris about data leakage, and its major impact upon machine learning models. It’s the kind of topic that we don’t often think about, but which can ruin our results. Raj discusses how to use activation maps and image embedding to find leakage, so that leaking information in our test set does not find its way into our training set.

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