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About Lecture Week 2 KNN and Model Evaluation PDF
This document provides an overview of lecture 2 of an introductory machine learning course. The lecture covers k-nearest neighbors (k-NN) classification and model evaluation techniques. It discusses how k-NN works by finding the k closest training examples to make predictions, and how to choose k and handle different data types. Examples of using k-NN for loan default prediction and activity recognition from wearable data are provided. The lecture also reviews metrics for evaluating models, including root mean squared error, confusion matrices, and F1 score.
Detailed Information
| Author: | ['Hoàng Phạm'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 53 |
| Language: | English |
| Format: | |
| Price: | FREE |
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