K-Nearest Neighbor 편집하기

'''K-Nearest Neighbo'''r, often abbreviated as '''K-NN''', is a simple and intuitive [[Classification Algorithm|classification]] and [[Regression Algorithm|regression algorithm]] used in [[Supervised Machine Learning|supervised machine learning]]. It classifies new data points based on the majority class among its nearest neighbors in the feature space. K-NN is a non-parametric algorithm, meaning it makes no assumptions about the underlying data distribution, making it versatile but often computationally intensive.

==How It Works==

The K-NN algorithm works by calculating the distance between the new data point and existing points in the dataset. Some common distance metrics used include:

*'''Euclidean Distance''': The straight-line distance between two points, most commonly used in K-NN.
*'''Manhattan Distance''': The distance calculated along grid lines, useful in certain types of data where this metric makes more sense.
*'''Minkowski Distance''': A generalized form of both Euclidean and Manhattan distances, where the parameter can be tuned based on the dataset.

The algorithm follows these steps:

1. '''Choose K''': Select the number of neighbors (K) to consider. This parameter is often tuned based on validation data to achieve the best performance.

2. '''Calculate Distances''': Compute the distance between the new data point and all points in the dataset.

3. '''Identify Nearest Neighbors''': Identify the K points with the shortest distance to the new data point.

4. '''Make Prediction''': For classification, assign the majority class among the K neighbors to the new point. For regression, calculate the average of the neighbors’ values.

==Applications of K-NN==

K-NN is widely used in applications where interpretability and simplicity are important. Some common use cases include:

*'''[[Recommendation System|Recommendation Systems]]''': K-NN is used to find items similar to a user’s preferences by comparing items with similar features.
*'''Image Recognition''': Classifying objects in images by identifying similar pixel patterns among labeled images.
*'''Medical Diagnosis''': Predicting diseases by comparing new patient data to known cases with similar symptoms or test results.

==Advantages and Disadvantages==

'''Advantages:'''
*'''Simplicity''': Easy to understand and implement.
*'''No Training Phase''': Since K-NN is a lazy learner, it doesn’t require a training phase, making it useful for certain real-time applications.
*'''Versatility''': Works for both classification and regression tasks.

'''Disadvantages:'''
*'''Computationally Intensive''': For large datasets, K-NN can be slow since it calculates distances for each prediction.
*'''Sensitive to Outliers''': Outliers can impact the results as K-NN considers all neighbors equally.
*'''Need for Feature Scaling''': Performance of K-NN depends on scaling, as features with larger scales could dominate distance calculations.

==Choosing the Right K Value==

Selecting the optimal K value is crucial for the performance of K-NN. Generally:
*A smaller K value may lead to [[overfitting]], as it considers fewer data points and may capture noise.
*A larger K value can smooth out the prediction but may also lead to [[underfitting]].

Common practices for finding the best K include using cross-validation on a range of K values to identify the one that provides the best accuracy on validation data.
[[Category:Data Science]]
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