Update
Definition of Update Update: Update is the process of modifying an existing item in a dataset. This may be done to correct errors, add new information, or improve the accuracy of the data.
Untested
Definition of Untested Untested: Untested refers to an algorithm or technique that has not been used on any data set.
Unsupervised Learning
Definition of Unsupervised Learning Unsupervised Learning: Unsupervised learning is a type of machine learning algorithm that does not rely on feedback from humans to learn how to identify patterns in data. These algorithms are typically used to identify patterns in data that have not been labeled or categorized by humans.
Unsampled
Definition of Unsampled Unsampled: Unsampled data is data that has not been selected or drawn from a population. This term is used in statistics and data science, where it usually refers to the selection of a random sample from a population.
Unknown
Definition of Unknown Unknown: Unknown is a term used in data science to describe an attribute or value that has yet to be determined. This may be due to a lack of data or because the data is too noisy to be accurately analyzed. In either case, the goal of data science is to identify…
Universality
Definition of Universality Universality: Universality is the property of being applicable to a class of objects or phenomena. In data science, this means that a model or algorithm can be used to solve a problem for a wide range of data sets. This makes universality an important property for any data science toolkit.
Univariate
Definition of Univariate Univariate: A univariate analysis is a data analysis technique that considers only one variable at a time.
Unique
Definition of Unique Unique: Unique refers to the attribute of a data set that represents a distinct value. In other words, a value is unique if it does not occur more than once in the data set.
Type II Error
Definition of Type II Error Type II error: A type II error, also known as a false negative error, is the incorrect acceptance of a false null hypothesis.
Type I Error
Definition of Type I Error Type I error: A type I error, also known as a false positive error, is the incorrect rejection of a true null hypothesis.