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.
Type I error: A type I error, also known as a false positive error, is the incorrect rejection of a true null hypothesis.
Definition of Natural Language Processing (NPL) Natural Language Processing (NPL): Natural language processing (NLP) is a field of computer science and linguistics that deals with the interaction between computers and human languages, and with the development of software that can understand natural language.
Definition of Decision Decision: A decision is a choice that is made between two or more possibilities. What are Decisions used for? Decisions are used to make choices or reach conclusions. They are a crucial part of the data science and machine learning process, since they allow us to make decisions based on information collected…
Definition of Prior Distribution A prior distribution is a probability distribution that is used to assign a probability to each outcome in a problem before any data is observed. This distribution is usually chosen based on experience or intuition.
Definition of Frequentist Frequentist: A Frequentist is someone who believes that the only valid methods of statistics are those that rely on the law of large numbers, and the principle of population stabilization. What do Frequentist do? Frequentists are data scientists and machine learning specialists who use frequentist statistical methods for their work. Frequentist methods…
Definition of Data Lake Data Lake: A data lake is a term used in big data management to describe a storage repository that holds a large volume of raw data in its native format. The data in a data lake can be processed and analyzed by the business users who own it, without having to…
Definition of False Positive False Positive: False positive is a result that incorrectly identifies an event as being positive. What are False Positive used? False Positive is a term used in data science and machine learning that refers to an incorrect classification of an item as being positive when, in reality, it is negative. It…