In Python, a variable is a named location in memory that stores a value. It can hold different types of data such as numbers, strings, lists, tuples, or even complex objects.

Variable (Python)

In statistics, variance measures the spread or dispersion of a set of data points. It quantifies how much the data points deviate from the mean (average) of the dataset.

Variance (statistics)

Variance

In machine learning, "validation" is the process of evaluating a model's performance on a data that it was not trained on, to assess its ability to generalize to new, unseen data.

Validation (Machine Learning)

Validation

Validation Loss is a metric used to evaluate the performance of a trained model on a validation set. It is computed by comparing the model's predictions to the true labels.

Validation Loss

A validation set is a subset of the labeled data for assessing the performance of a model during the training phase. It independently evaluates the model's ability to generalize.

Validation Set

The Vanishing Gradient Problem occurs when the gradients of the loss function with respect to the weights become very small, causing the weights to be updated very slowly or not at all.

Vanishing Gradient Problem

Variable importances, also known as feature importances or attribute importances, refer to a measure of the contribution of individual variables in a  model to its performance.

Variable Importances

A Variational Autoencoder (VAE) is a type of generative model. VAEs belong to the family of autoencoders but incorporate probabilistic and variational methods to model complex distributions in the latent space.