Bootstrap
Bootstrap is a technique for estimating the parameters of a statistical model from a limited number of observations. It is a resampling technique that is used to build confidence intervals or to generate…
Boosting
Boosting is a machine learning technique for improving the accuracy of predictions. It is a type of ensemble learning, which combines the predictions of several individual models in order to produce a…
Bivariate Analysis
Bivariate Analysis is the examination of two sets of data, typically in order to identify any correlations between them. This can be used to inform further analysis, or to gain a better understanding of the data…
Big Data
Big Data is a term used to describe the large volume of data that organizations collect and store. The term was first coined in 2001 by Doug Laney, an analyst at Gartner, and it was originally used…
Bayesian Inference
Bayesian Inference is a statistical method of using prior knowledge and data to generate probabilities about future events.
Bagging
Bagging is a technique for improving the accuracy of predictions made by a machine learning algorithm. It works by training multiple models on different subsets of the data, and then combining…
Adaptive Boosting
Adaptive boosting, also known as AdaBoost, is a machine learning algorithm used to improve the accuracy of a classification model. It works by iteratively training a series of weak classifiers on a dataset, and then combining their predictions to produce a more accurate final prediction.
Analytics
Analytics is the process of examining data to uncover hidden patterns and insights. It can be used to improve business decisions, understand customers, detect fraud or to understand what is happening in the world.
Algorithm
An algorithm is a step-by-step procedure for solving a problem or accomplishing a task.
Additive Smoothing
Additive smoothing: Additive smoothing is a technique used in data science to smooth out noisy datasets. It is a form of noise reduction, and it works by adding a small amount of noise to the dataset in order to obscure the original noise. This makes it easier to identify the underlying trends in the data.