ML-For-Beginners/4-Classification/3-Classifiers-2

Recipe Classifiers 2

In this second Classification lesson, you will explore more ways to classify data, and the ramifications for choosing one over the other.

Pre-lecture quiz

Scikit-Learn offers a similar, but more granular cheat sheet that can further help narrow down your estimators (another term for classifiers):

This map is very helpful as you can 'walk' along its paths to a decision:

• We have >50 samples
• We want to predict a category
• We have labeled data
• We have fewer than 100K samples
• We can choose a Linear SVC
• If that doesn't work, since we have numeric data
• We can try a KNeighbors Classifier and if that doesn't work, try SVC and Ensemble Classifiers

This is a terrific trail to try. For our first foray, explore how well

Introduction

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Notes

Prerequisite

What steps should have been covered before this lesson?

Preparation

Preparatory steps to start this lesson

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[Step through content in blocks]

[Topic 1]

Task:

Work together to progressively enhance your codebase to build the project with shared code:

code blocks

✅ Knowledge Check - use this moment to stretch students' knowledge with open questions

[Topic 2]

[Topic 3]

🚀Challenge

Add a challenge for students to work on collaboratively in class to enhance the project

Optional: add a screenshot of the completed lesson's UI if appropriate

Post-lecture quiz

Review & Self Study

Assignment

Assignment Name