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This KNN Algorithm in Machine Learningtutorial will help you understand what is KNN, why do we need KNN, and how KNN algorithm works using Python. You will learn how do we choose the factor 'K', when do we use KNN, with proper hands on demonstration to predict whether a person will have diabetes or not, using the KNN algorithm.
Below topics are explained in this K-Nearest Neighbor Algorithm (KNN Algorithm) tutorial:
00:00 Introduction to KNN(K Nearest Neighbor)
00:57 Why do we need KNN?
02:33 What is KNN?
03:51 How do we choose the factor 'K'?
05:46 When do we use KNN?
06:42 How does the KNN algorithm work?
09:19 Use case - Predict whether a person will have diabetes or not?
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When Do We Use the KNN Algorithm?
The KNN algorithm is used in the following scenarios:
✅Data is labeled
✅Data is noise-free
✅Dataset is small, as KNN is a lazy learner
Pros and Cons of Using KNN
✅Pros: Since the KNN algorithm requires no training before making predictions, new data can be added seamlessly, which will not impact the accuracy of the algorithm.
KNN is very easy to implement. There are only two parameters required to implement KNN—the value of K and the distance function (e.g. Euclidean, Manhattan, etc.)
✅Cons: The KNN algorithm does not work well with large datasets. The cost of calculating the distance between the new point and each existing point is huge, which degrades performance.
Feature scaling (standardization and normalization) is required before applying the KNN algorithm to any dataset. Otherwise, KNN may generate wrong predictions.
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