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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 algorithmโs 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.