Definition of Feature Engineering Feature Engineering: Feature engineering is the process of transforming raw data into a form that is more amenable to analysis or machine learning. This can involve things like aggregating data, transforming variables, or creating new features from existing variables. Feature engineering is an important part of data science, as it can…
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…
Definition of False Negative False Negative: False negative is a result of a test where a condition that is true is incorrectly reported as being false. What are the impacts of False Negatives? False Negatives can have a significant impact and be costly to data science and machine learning projects. False Negatives refer to instances…
Definition of Facet Facet: A facet is a property or attribute of an object that can be measured or quantified. In data science, facets are often used to group and filter data sets based on specific criteria. For example, a data set might be grouped by country of origin, age group, or income level. How…
Definition of Exploratory Data Analysis Exploratory Data Analysis: Exploratory data analysis (EDA) is the examination of data to summarize, visualize, and discover patterns. EDA is used to identify which variables are important and to develop hypotheses about the relationships between variables. What is an Exploratory Data Analysis used for? An Exploratory Data Analysis (EDA) is…
Definition of Expectation Maximization Expectation Maximization: Expectation Maximization (EM): A statistical algorithm used to find the maximum likelihood estimate of a parameter in a probabilistic model. EM iteratively maximizes the expected likelihood of the data under the model, by adjusting the model’s parameters. What is Expectation Maximization used for? Expectation Maximization (EM) is a statistical…
Definition of Exact p-value Exact p-value: The Exact p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. What is Exact p-value used for? Exact p-values are used to help assess the strength of evidence for a hypothesis…
Definition of Exact Match Exact Match: Exact match is a term used in data science to describe a type of search algorithm that compares two strings of text and determines whether or not they are an exact match. What is an Exact Match used for? An Exact Match is a type of data matching algorithm…
Definition of Evaluation Evaluation: Evaluation is the process of assessing how well a model or system is performing, typically by measuring its accuracy, precision, recall, or some other performance metric. Evaluation is an important part of the data science process, as it allows you to determine whether your models are meeting your expectations and helping…
Definition of Euclidean Distance Euclidean Distance: The Euclidean distance between two points is the length of the straight line between them. What is Euclidean Distance used for? Euclidean Distance is a mathematical tool used to measure the distance between two points in a multidimensional space. It is also known as the “straight line” or “as-the-crow-flies”…