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68 lines
1.7 KiB
68 lines
1.7 KiB
# Recipe Classifiers 2
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In this second Classification lesson, you will explore more ways to classify data, and the ramifications for choosing one over the other.
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
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Scikit-Learn offers a similar, but more granular cheat sheet that can further help narrow down your estimators (another term for classifiers):
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This map is very helpful as you can 'walk' along its paths to a decision:
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- We have >50 samples
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- We want to predict a category
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- We have labeled data
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- We have fewer than 100K samples
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- We can choose a Linear SVC
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- If that doesn't work, since we have numeric data
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- We can try a KNeighbors Classifier and if that doesn't work, try SVC and Ensemble Classifiers
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This is a terrific trail to try. For our first foray, explore how well
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### Introduction
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Describe what will be covered
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> Notes
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### Prerequisite
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What steps should have been covered before this lesson?
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### Preparation
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Preparatory steps to start this lesson
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---
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[Step through content in blocks]
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## [Topic 1]
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### Task:
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Work together to progressively enhance your codebase to build the project with shared code:
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```html
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code blocks
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```
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✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
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## [Topic 2]
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## [Topic 3]
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## 🚀Challenge
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Add a challenge for students to work on collaboratively in class to enhance the project
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Optional: add a screenshot of the completed lesson's UI if appropriate
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
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## Review & Self Study
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## Assignment
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[Assignment Name](assignment.md)
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