Last Updated on August 15, 2020 Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post you will discover the logistic regression algorithm for machine learning. After reading this post you will know: The many names and terms used when describing logistic regression (like log odds and logit). The representation used for a logistic regression model. Techniques used to […]

Last Updated on August 12, 2019 Logistic regression is one of the most popular machine learning algorithms for binary classification. This is because it is a simple algorithm that performs very well on a wide range of problems. In this post you are going to discover the logistic regression algorithm for binary classification, step-by-step. After reading this post you will know: How to calculate the logistic function. How to learn the coefficients for a logistic regression model using stochastic gradient […]

Last Updated on August 15, 2020 Logistic regression is a classification algorithm traditionally limited to only two-class classification problems. If you have more than two classes then Linear Discriminant Analysis is the preferred linear classification technique. In this post you will discover the Linear Discriminant Analysis (LDA) algorithm for classification predictive modeling problems. After reading this post you will know: The limitations of logistic regression and the need for linear discriminant analysis. The representation of the model that is learned […]

Last Updated on August 15, 2020 Decision Trees are an important type of algorithm for predictive modeling machine learning. The classical decision tree algorithms have been around for decades and modern variations like random forest are among the most powerful techniques available. In this post you will discover the humble decision tree algorithm known by it’s more modern name CART which stands for Classification And Regression Trees. After reading this post, you will know: The many names used to describe the […]

Last Updated on August 15, 2020 Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling. In this post you will discover the Naive Bayes algorithm for classification. After reading this post, you will know: The representation used by naive Bayes that is actually stored when a model is written to a file. How a learned model can be used to make predictions. How you can learn a naive Bayes model from training data. How to best prepare […]

Last Updated on August 12, 2019 Naive Bayes is a very simple classification algorithm that makes some strong assumptions about the independence of each input variable. Nevertheless, it has been shown to be effective in a large number of problem domains. In this post you will discover the Naive Bayes algorithm for categorical data. After reading this post, you will know. How to work with categorical data for Naive Bayes. How to prepare the class and conditional probabilities for a Naive […]

Last Updated on August 15, 2020 In this post you will discover the k-Nearest Neighbors (KNN) algorithm for classification and regression. After reading this post you will know. The model representation used by KNN. How a model is learned using KNN (hint, it’s not). How to make predictions using KNN The many names for KNN including how different fields refer to it. How to prepare your data to get the most from KNN. Where to look to learn more about […]

Last Updated on August 15, 2020 A downside of K-Nearest Neighbors is that you need to hang on to your entire training dataset. The Learning Vector Quantization algorithm (or LVQ for short) is an artificial neural network algorithm that lets you choose how many training instances to hang onto and learns exactly what those instances should look like. In this post you will discover the Learning Vector Quantization algorithm. After reading this post you will know: The representation used by […]