simple-kNN-1.1.6

Simple kNN algorithm with k-Fold Cross Validation
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Summary

Project Name simple-kNN Project Url https://github.com/chaitanyakasaraneni/simple-kNN
Publish Time 2020-10-11 16:18:18 MD5 Code a50a0f38953ccc5fbae5bfbd16b8d4e5
开源协议 Jar Size 134.5 KB
Python Version source Require Python >=3.6
文件类型 sdist 下载地址 simple_kNN-1.1.6.tar.gz
install

项目介绍

# simple-kNN [![pypi](https://img.shields.io/pypi/v/simple-kNN?color=red&label=PyPI&style=for-the-badge)](https://pypi.python.org/pypi/simple-kNN) [![ci](https://img.shields.io/github/workflow/status/chaitanyakasaraneni/simple-kNN/Continuous%20Integration?&label=Continuous%20Integration&logo=GitHub&style=for-the-badge)](https://pypi.python.org/pypi/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](https://github.com/chaitanyakasaraneni/knnFromScratch) #### *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 simple-kNN ``` ## Usage ``` from simple_kNN.distanceMetrics import distanceMetrics from simple_kNN.kFoldCV import kFoldCV from simple_kNN.kNNClassifier import kNNClassifier ``` #### References - My [medium article on building kNN from scratch](https://link.medium.com/BV27Pox3qab) - More info on Cross Validation can be seen [here](https://medium.com/datadriveninvestor/k-fold-and-other-cross-validation-techniques-6c03a2563f1e) - [kNN](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) - [kFold Cross Validation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html)