How to implement KNN from scratch with Python

TL;DR

K Nearest Neighbors Algorithm is a classification/regression technique based on proximity to surrounding data points.

Transcript

the first algorithm we're going to look into is k n or k nearest neighbors how knm works it's basically given a data point you calculate this data point distance from all other data points in your data set and then you get the closest k points so this k is a hyper parameter that the user determines and in regression to get the results you get the a... Read More

Key Insights

• 😥 K Nearest Neighbors (KNN) leverages the proximity of data points for classification or regression.
• 👌 The K value in KNN determines the number of nearest neighbors considered.
• 🏛️ Implementing KNN in Python involves creating a class with fit and predict functions.
• ❓ Euclidean distance calculation and majority voting are essential for KNN prediction accuracy.
• ✋ KNN can achieve high accuracy, as demonstrated with the Iris dataset.
• ⚾ KNN implementation can be optimized and customized based on specific needs.
• 🎰 KNN is a simple yet powerful algorithm for machine learning tasks.

Questions & Answers

Q: How does the K Nearest Neighbors algorithm work?

KNN calculates distances between a data point and all others, selects the k closest points, and uses majority voting for classification or averaging for regression.

Q: How is the K value determined in the K Nearest Neighbors algorithm?

The K value in KNN is a hyperparameter set by the user to define the number of closest neighbors considered for classification or regression tasks.

Q: What is the difference between K Nearest Neighbors for regression and classification?

In regression, KNN averages the values of the k neighbors for prediction, while in classification, it uses the majority vote of the k neighbors to determine the label.

Q: How is the K Nearest Neighbors algorithm implemented in Python?

The KNN algorithm can be implemented in Python by creating a class with fit and predict functions, calculating distances using Euclidean distance, and using majority voting for prediction.

Summary & Key Takeaways

• K Nearest Neighbors (KNN) algorithm calculates distances from a data point to others in the dataset, uses the k closest points for classification or regression.

• The algorithm is implemented in Python by creating a class with fit and predict functions, using Euclidean distance and majority voting for prediction.

• An example using the Iris dataset showcases KNN classification with an accuracy of 96%.