Sam Jang's Blog

K Nearest Neighbors Hello! In this post, we will be exploring a very simple classification algorithm called 'K Nearest Neighbors'. How K Nearest Neighbors work: The green point above is the testing data and the blue and red points are training data in different classes. The way KNN works is by taking the Euclidean distance from your test data(green point) to each of your training data and classify your test data by the class of the nearest point to the testing data. Euclidean Distance: The Euclidean distance is basically the distance between two points on the Euclidean space. To put simply, the distance between two points.  Formula : K K: Parameter that takes a certain number of nearest points to use for the classification process.  K basically takes 'k' number of nearest points(training data) and classifies the test data by the class that has the highest vote. For example, if ...

Classification vs Regression Classification: Classification algorithms attempt to predict a discrete class label. For example, a problem where you are trying to build a model(or algorithm) to interpret whether an image is a dog or a cat would be a classification problem.    Some Common Classification Machine Learning Algorithms/models : - K Nearest Neighbors - Support Vector Machine - Logistic Regression - Random Forest Classifier - Decision Trees - Neural Networks Regression: On the other hand, regression algorithms attempt to predict a continuous quantity. An example of a regression problem would be when you're trying to predict the prices of homes, where the prediction could be an integer, fraction, or decimal number.  Some Common Regression Machine Learning Algorithms/models: - Linear Regressor - Polynomial Regressor - Lasso Regressor - Ridge Regressor - Elastic Net Regressor - Support Vector Regressor - Regression Trees - Neural Networks ...