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39 lines
2.0 KiB
39 lines
2.0 KiB
# Introduction to Classification
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In these four lessons, you will discover the 'meat and potatoes' of classic machine learning - Classification. No pun intended - we will walk through using various classification algorithms with a dataset all about the brilliant cuisines of Asia. Hope you're hungry!
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Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with Regression techniques. If machine learning is all about assigning names to things via datasets, then classification generally falls into two groups: binary classification and multiclass classfication.
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Remember, Linear Regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?
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Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this recipe data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
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[](https://youtu.be/eg8DJYwdMyg "Introduction to Classification")
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> 🎥 Click the image above for a video: MIT's John Guttag introduces Classification
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## [Pre-lecture quiz](link-to-quiz-app)
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### Introduction
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Before working to clean the data and prepare it for analysis, it's useful to understand several of the algorithms that you will use.
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- Support-vector machines
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- Naive Bayes
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- Decision trees
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- K-nearest neighbor algorithm
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✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
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## 🚀Challenge
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## [Post-lecture quiz](link-to-quiz-app)
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## Review & Self Study
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## Assignment
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[Assignment Name](assignment.md)
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