Non-Negative Matrix Factorization: Non-negative Matrix Factorization (NMF) is a technique used to decompose a matrix into two parts: a lower triangular matrix (containing the “feature” values) and an upper triangular matrix (containing the “coefficients” associated with each feature).
Definition of Discrete Variable A discrete variable is a variable that can only take on certain, specific values. Contrast this with a continuous variable, which can take on any value within a given range. Discrete variables are often used in statistical models to represent outcomes that can only happen a certain number of times, or…
Definition of Mean Absolute Error Mean Absolute Error (MAE) is a measure of the accuracy of predictions made by a model. It is computed by taking the average of the absolute differences between the predicted values and the actual values for each observation. How is Mean Absolute Error used? Mean Absolute Error (MAE) is a…
Definition of Ensembling Ensembling: Ensembling is a technique used in machine learning that consists of combining the predictions of multiple models in order to improve the accuracy of the predictions. What is Ensembling used for? Ensembling is a technique in data science and machine learning in which multiple prediction models are combined to produce a…
Artificial intelligence (AI) is the ability of a computer program to learn from data, recognize patterns and make decisions with minimal human intervention
Definition of Entropy Entropy: Entropy is a measure of the unpredictability of a system. In information theory, entropy ( ) is a measure of the uncertainty associated with a random variable. Entropy is defined as the average amount of information that is not known about the value of a random variable. When is Entropy used?…
Definition of Normal Distribution A normal distribution is a type of bell-shaped distribution in which the majority of the data falls around the mean. This distribution is often used in statistics to model real-world data.