![]() ![]() Classification ModelsĪ Classification Model is simply a mathematical tool to determine what category or class of something you’re dealing with based on a set of variables or inputs. There are lots of complicated ways to measure error and test models but as long as you get the basic idea we can keep going. While the training set helps to develop the model, the test set tries it out in a real world scenario and sees how well it fares. ![]() In other words - garbage in, garbage out.Ī test set is typically a subset of the training data in that it also contains all variables and the correct classifications. ![]() The more accurate your training data and the more of it you have the better. It’s important to remember that machine learning models are only as good as the training data. Training sets can be developed in a variety of ways but in this tutorial, we’ll be using a training set that was classified by a human expert. Training data is a data set that contains all of the variables we have available as well as the correct classification. Machine Learning algorithms adapt the model based on a set of training data. ![]() Don’t worry if you’re not fully clear right now, by the end of the tutorial you’ll know exactly what I’m talking about. In this tutorial we’ll be applying machine learning to a classification model. In other words, Machine Learning takes the models we’ve built and uses real world data to “learn” how to fine tune the parameters of the model to be most useful in a real world scenario based on the training data. Machine Learning is a collection of techniques to optimize models. Don’t get overwhelmed, let’s break down what that means bit by bit. The end goal of this tutorial is to use Machine Learning to build a classification model on a set of real data using an implementation of the k-nearest neighbors (KNN) algorithm. ![]()
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