Understanding K-Nearest Neighbors (KNN) Regression

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Introduction to KNN Regression

K-Nearest Neighbors (KNN) regression is a type of instance-based learning algorithm used for regression problems. It makes predictions based on the ๐‘˜ most similar instances (neighbors) in the training dataset. The algorithm is non-parametric, meaning it makes predictions without assuming any underlying data distribution.

Key Concepts

1. Distance Metric: The method used to calculate the distance between instances. Common metrics include Euclidean, Manhattan, and Minkowski distances.

2. k: The number of neighbors to consider when making a prediction. Choosing the right ๐‘˜ is crucial for the algorithmโ€™s performance.

3. Weighted KNN: In some variants, closer neighbors have a higher influence on the prediction than more distant ones, often implemented by assigning weights inversely proportional to the distance.

How KNN Regression Works

Step-by-Step Process

1. Load the Data: Start with a dataset consisting of feature vectors and their corresponding target values.

2. Choose the Number of Neighbors (k): Select the number of nearest neighbors to consider for making predictions.

13. Distance Calculation: For a new data point, calculate the distance between this point and all points in the training dataset.

4. Find Nearest Neighbors: Identify the ๐‘˜ points in the training data that are closest to the new point.

5. Predict the Target Value: Compute the average (or a weighted average) of the target values of the ๐‘˜ nearest neighbors.

Example:

To walk you through an example, follow this link to our official blog site to continue with examples and visuals.