# 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](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)

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

![ML Map from Scikit-Learn](images/map.png)

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

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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:

```html
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](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
## Review & Self Study

## Assignment 

[Assignment Name](assignment.md)