Class KNearestNeighborsOptions
Configuration options for the K-Nearest Neighbors algorithm, which makes predictions based on the values of the K closest data points in the training set.
public class KNearestNeighborsOptions : NonLinearRegressionOptions- Inheritance
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KNearestNeighborsOptions
- Inherited Members
Remarks
K-Nearest Neighbors (KNN) is a simple but powerful non-parametric algorithm that can be used for both classification and regression. For regression, it predicts the value of a new data point by averaging the values of its K nearest neighbors in the training data. The algorithm doesn't build an explicit model during training - instead, it stores the training data and performs calculations at prediction time, making it an example of "lazy learning."
For Beginners: K-Nearest Neighbors (KNN) is one of the simplest machine learning algorithms to understand. It works on a very intuitive principle: things that are similar tend to have similar outcomes.
Imagine you want to predict the price of a house. With KNN, you would:
- Find the K houses in your training data that are most similar to the house you're trying to price (based on features like size, location, number of bedrooms, etc.)
- Take the average price of those K houses as your prediction
The "K" in KNN is simply how many neighbors you consider when making your prediction. If K=5, you look at the 5 most similar houses.
KNN is different from many other algorithms because it doesn't build a complex model during training. Instead, it simply remembers all the training examples and uses them directly when making predictions. This makes it easy to understand but can make it slower for predictions on large datasets.
This class inherits from NonLinearRegressionOptions, so all the general non-linear regression settings are also available. The additional setting specific to KNN lets you configure how many neighbors to consider when making predictions.
Properties
K
Gets or sets the number of nearest neighbors to consider when making predictions.
public int K { get; set; }Property Value
- int
The number of neighbors, defaulting to 5.
Remarks
This parameter defines how many of the closest training examples are used to generate a prediction. A smaller K value makes the model more sensitive to local patterns but may lead to overfitting and sensitivity to noise. A larger K value produces smoother predictions but may miss important local patterns in the data.
For Beginners: This setting controls how many "neighbors" (similar data points) the algorithm considers when making a prediction. With the default value of 5, the algorithm will find the 5 most similar examples in your training data and average their values to make its prediction.
Choosing the right value for K involves a trade-off:
Smaller K values (like 1 or 3): The model becomes more detailed and can capture very specific patterns in your data. However, it also becomes more sensitive to noise or unusual data points. Think of it like asking just a few neighbors about local restaurants - you might get very specific recommendations, but if one neighbor has unusual tastes, it could skew your results.
Larger K values (like 10 or 20): The model makes smoother, more generalized predictions that are less affected by individual outliers. However, it might miss important local patterns. This is like asking many neighbors about restaurants - you'll get a more general consensus, but might miss some hidden gems that only a few people know about.
The default value of 5 is a good starting point for many problems. You might want to try different values and see which gives the best performance for your specific dataset. As a rule of thumb, an odd number for K can be helpful when doing classification (to avoid ties in voting), but for regression tasks like predicting continuous values, both even and odd values work fine.