The Bernoulli Naive Bayes is one of the variations of the Naive Bayes algorithm in machine learning and it is very useful to use in a binary distribution where the output label may be present or absent. If you have…

F-beta is the weighted harmonic mean of the precision and recall. It is used as a performance evaluation measure for classification-based machine learning models. If you’ve never used this performance measurement metric before to evaluate your classification models, this article…

Passive Aggressive Regression belongs to the category of online learning in machine learning. It is not one of the most commonly used machine learning algorithms, but it can nonetheless be used to achieve efficient results that solve regression-based problems. So,…

Stochastic gradient descent is a machine learning algorithm that is used to minimize a cost function by iterating a weight update based on the gradients. If you’ve never used the SGD classification algorithm before, this article is for you. In…

In machine learning, variance is the difference between the actual samples of the dataset and the predictions made by the model. When working on a regression-based machine learning problem, it is very useful to know how much of the variance…

The R2 score is one of the performance evaluation measures for regression-based machine learning models. It is also known as the coefficient of determination. If you want to learn how to evaluate the performance of a machine learning model using…

t-SNE is a very powerful machine learning algorithm that can be used to visualize a high-dimensional dataset also in two-dimensional figures. It stands for t-Distributed Stochastic Neighbor Embedding. If you want to learn more about t-SNE and how to visualize…

When we want to train a machine learning model, it is always recommended to divide the dataset into training and test sets if your dataset is large. If you are interested in learning how to split a dataset into training…

Independent Component Analysis (ICA) is one of the alternatives of PCA that is used to find the underlying factors or components from a multivariate statistical dataset. This is different from a standard PCA because it looks for components that are…

Non-Negative Matrix Factorization (NNMF) is a group of machine learning algorithms used in multivariate analysis and linear algebra. It is used in place of PCA when the dataset is composed of non-negative items. If you don’t know anything about the…