Simple kNN algorithm with k-Fold Cross Validation
Project description
simple-kNN
This repository is for Continuous Integration of my simple k-Nearest Neighbors (kNN) algorithm to pypi package.
For notebook version please visit this repository
k-Nearest Neighbors
k-Nearest Neighbors, kNN for short, is a very simple but powerful technique used for making predictions. The principle behind kNN is to use “most similar historical examples to the new data.”
k-Nearest Neighbors in 4 easy steps
- Choose a value for k
- Find the distance of the new point to each record of training data
- Get the k-Nearest Neighbors
- Making Predictions
- For classification problem, the new data point belongs to the class that most of the neighbors belong to.
- For regression problem, the prediction can be average or weighted average of the label of k-Nearest Neighbors
Finally, we evaluate the model using k-Fold Cross Validation technique
k-Fold Cross Validation
This technique involves randomly dividing the dataset into k-groups or folds of approximately equal size. The first fold is kept for testing and the model is trained on remaining k-1 folds.
Installation
pip install word_knn
References
- More info on Cross Validation can be seen here
- kNN
- kFold Cross Validation
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