# Classification: K-Nearest Neighbors

K-Nearest Neighbors is a supervised machine learning algorithm for classification. You will implement and test this algorithm on several datasets.

Start## Key Concepts

Review core concepts you need to learn to master this subject

K-Nearest Neighbors Underfitting and Overfitting

KNN Classification Algorithm in Scikit Learn

Euclidean Distance

Elbow Curve Validation Technique in K-Nearest Neighbor Algorithm

K-Nearest Neighbors

KNN of Unknown Data Point

Normalizing Data

K-Nearest Neighbors Underfitting and Overfitting

K-Nearest Neighbors Underfitting and Overfitting

The value of k in the KNN algorithm is related to the error rate of the model. A small value of k could lead to overfitting as well as a big value of k can lead to underfitting. Overfitting imply that the model is well on the training data but has poor performance when new data is coming. Underfitting refers to a model that is not good on the training data and also cannot be generalized to predict new data.

- 1In this lesson, you will learn three different ways to define the distance between two points: 1. Euclidean Distance 2. Manhattan Distance 3. Hamming Distance Before diving into the distance form…
- 2
**Euclidean Distance**is the most commonly used distance formula. To find the Euclidean distance between two points, we first calculate the squared distance between each dimension. If we add up al… - 3
**Manhattan Distance**is extremely similar to Euclidean distance. Rather than summing the squared difference between each dimension, we instead sum the absolute value of the difference between eac… - 4
**Hamming Distance**is another slightly different variation on the distance formula. Instead of finding the difference of each dimension, Hamming distance only cares about whether the dimensions a… - 5Now that you’ve written these three distance formulas yourself, let’s look at how to use them using Python’s SciPy library: - Euclidean Distance .euclidean() - Manhattan Distance .cityblock() - …

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## How you'll master it

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