Final Thoughts

Learn to evaluate the strengths and limitations of K-Nearest Neighbors, understand the importance of selecting the right k parameter, and explore handling data without distribution assumptions. This lesson also addresses tie-breaking strategies and computational efficiency for large datasets.

We'll cover the following...

Parametric and nonparametric models
Breaking ties

Advantages and Disadvantages of KNN

Advantages	Disadvantages
It's simple to understand and explain.	All training data must be stored.
Model training is fast. KNN is a lazy learner, and the algorithm does not learn from the training data. It memorizes the training dataset.	The prediction phase can be slow when test data is large.
It can be used for classification and regression.	It's sensitive to irrelevant features and feature scaling.
It’s a nonlinear algorithm: The decision boundaries of KNN are locally linear segments but generally have a complex shape that is not equivalent to a line in 2D or a hyperplane in higher dimensions.	Accuracy is (generally) not competitive with the best supervised learning methods.

1.Course Introduction

2.Linear Regression

3.Regularization

4.Bias-Variance Trade-off

5.Categorical Features

6.Logistic Regression

7.Logistic Regression: Titanic Data

Project

8.Multiclass Classification and Handling Imbalanced Classes

9.Project: Predicting Chronic Kidney Disease

10.K-Nearest Neighbors

11.Implementation of K-Nearest Neighbors

12.Logistic Regression vs. KNN

13.Decision Tree Learning

Project

14.Bootstrapping and Confidence Interval

15.Support Vector Machine

16.Practice and Comparisons

17.What's Next?

18.Appendix

Final Thoughts

Advantages and Disadvantages of KNN