- Naive Bayes
- Natural Language Processing (NLP)
- Nearest Neighbor
- Negative Sampling
- Network Compression
- Network Science
- Network Topology
- Network Visualization
- Neural Architecture Search
- Neural Collaborative Filtering
- Neural Differential Equations
- Neural Machine Translation
- Neural Networks
- Neural Style Transfer
- Neural Tangent Kernel
- Neuroevolution
- Neuromorphic Computing
- Node Embedding
- Noise Reduction Techniques
- Non-autoregressive models
- Non-negative Matrix Factorization
- Non-parametric models
- Nonlinear Dimensionality Reduction
- Nonlinear Regression
- Nonparametric Regression
- Normalization
- Novelty Detection
- Numerical Methods
- Numerical Optimization
What is Nearest Neighbor
Understanding Nearest Neighbor Algorithm
Nearest neighbor algorithm is one of the simplest algorithms used for classification and regression problems. It is a lazy learning algorithm that works by finding the most similar data point(s) in the training set to a new data point and assigns the closest label or value to the new point.
How does Nearest Neighbor algorithm work?
The first step in the algorithm is to find the distance between the new point and all the points in the training set. Euclidean distance is the most commonly used distance metric. It is the square root of the sum of the squared differences between the corresponding values of two points.
Euclidean distance formula:√((a1-b1)^2 + (a2-b2)^2 + ………..+ (an-bn)^2)
Where a1, a2,….., an are the feature values of the new point and b1, b2,……. bn are the feature values of each point in the training set.
After computing the distances, the algorithm selects the k points in the training set that are closest to the new point. k is typically any odd number to avoid ties. One can choose k by trying different values and selecting the one that gives the best performance on the validation set.
Finally, the algorithm assigns the label or value that is most common or average among the k neighbors to the new point.
Pros and Cons of Nearest Neighbor algorithm
- Pros
- The algorithm is easy to understand and implement.
- It can be used for classification and regression tasks.
- The model adapts easily to new data points without requiring to retrain the entire model.
- Cons
- It can be computationally expensive for large datasets.
- It is sensitive to irrelevant and redundant features, and noisy data points.
- The algorithm may overfit or underfit the data if the value of k is not optimized.
Applications of Nearest Neighbor algorithm
Nearest Neighbor algorithm has a wide range of applications in various fields, including:
- Image recognition: For identifying similar images in a database based on the features of an input image.
- Recommendation systems: For recommending products, movies, songs, etc., to users based on their previous searches or purchases.
- Speech recognition: For converting speech to text by comparing the audio signal with pre-recorded speech.
- Time series forecasting: For predicting future values of a time series by comparing current values with historical values.
- Medical diagnosis: For identifying diseases based on a patient's symptoms and medical history.
Conclusion
Nearest neighbor algorithm is a simple yet powerful algorithm that can be used for classification and regression tasks. It works by finding the most similar data points to a new point and assigning the closest label or value to it. While the algorithm has several advantages such as easy implementation and adaptability to new data, it also has some disadvantages such as sensitivity to irrelevant features and computational overhead. Nearest neighbor algorithm finds wide applications in image recognition, recommendation systems, speech recognition, time series forecasting, and medical diagnosis.