Terms and definitions from all courses

Bayes’ theorem: An equation that can be used to calculate the probability of an outcome or class, given the values of predictor variables
Bayesian inference: (Refer to Bayesian statistics)
Bayesian statistics: A powerful method for analyzing and interpreting data in modern data analytics; also referred to as Bayesian inference


Best fit line: The line that fits the data best by minimizing some loss function or error


Bias: In data structuring, refers to organizing data results in groupings, categories, or variables that are misrepresentative of the whole dataset


Bias-variance trade-off: Balance between two model qualities, bias and variance, to minimize overall error for unobserved data


Bin: A segment of data that groups values into categories


Binning: Grouping continuous values into a smaller number of categories, or intervals Binomial distribution: A discrete distribution that models the probability of events with only two possible outcomes: success or failure
Binomial logistic regression: A technique that models the probability of an observation falling into one of two categories, based on one or more independent variables


Binomial logistic regression linearity assumption: An assumption stating that there should be a linear relationship between each X variable and the logit of the probability that Y equals one


Black-box model: Any model whose predictions cannot be precisely explained Boolean: A data type that has only two possible values, usually true or false


Boolean data: A data type that has only two possible values, usually true or false Boolean masking: A filtering technique that overlays a Boolean grid onto a dataframe in order to select only the values in the dataframe that align with the True values of the grid


Boosting: A technique that that builds an ensemble of weak learners sequentially, with each consecutive learner trying to correct the errors of the one that preceded it Bootstrapping: Refers to sampling with replacement