Heteroscedasticity
Heteroscedasticity is a condition in statistical models where the variability of a variable is unequal across the range of values of a second variable that predicts it.
Hidden Markov Model
A Hidden Markov Model (HMM) is a statistical model used to represent systems that are assumed to follow a Markov process with hidden states.
Hierarchical Clustering
Hierarchical Clustering is a method of cluster analysis that seeks to build a hierarchy of clusters.
Hinge Loss
Hinge Loss is a loss function used primarily in machine learning for "maximum-margin" classification models, notably support vector machines.
Homoscedasticity
Homoscedasticity is a statistical property where the variance of errors or residuals is constant across all levels of an independent variable.
Hyperparameter Tuning
Hyperparameter tuning is the process of selecting the optimal set of parameters that governs the learning process of a machine learning model.
Hypothesis Testing
Hypothesis testing is a statistical method used to determine the validity of a hypothesis by analyzing sample data.