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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
Describe what will be covered
Notes
Prerequisite
What steps should have been covered before this lesson?
Preparation
Preparatory steps to start this lesson
[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