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  2. Data set - Wikipedia

    en.wikipedia.org/wiki/Data_set

    Various plots of the multivariate data set Iris flower data set introduced by Ronald Fisher (1936). [1]A data set (or dataset) is a collection of data.In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question.

  3. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    There are several types of data cleaning, that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. [ 26 ] [ 27 ] Quantitative data methods for outlier detection, can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. [ 28 ]

  4. Statistics - Wikipedia

    en.wikipedia.org/wiki/Statistics

    Numerical descriptors include mean and standard deviation for continuous data (like income), while frequency and percentage are more useful in terms of describing categorical data (like education). When a census is not feasible, a chosen subset of the population called a sample is studied.

  5. Arithmetic mean - Wikipedia

    en.wikipedia.org/wiki/Arithmetic_mean

    The arithmetic mean of a set of observed data is equal to the sum of the numerical values of each observation, divided by the total number of observations. Symbolically, for a data set consisting of the values x 1 , … , x n {\displaystyle x_{1},\dots ,x_{n}} , the arithmetic mean is defined by the formula:

  6. Exploratory data analysis - Wikipedia

    en.wikipedia.org/wiki/Exploratory_data_analysis

    Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.

  7. Feature (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Feature_(machine_learning)

    In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. [citation needed]

  8. Data - Wikipedia

    en.wikipedia.org/wiki/Data

    The term data-driven is a neologism applied to an activity which is primarily compelled by data over all other factors. [citation needed] Data-driven applications include data-driven programming and data-driven journalism.

  9. List of numerical analysis topics - Wikipedia

    en.wikipedia.org/wiki/List_of_numerical_analysis...

    Geometric median — the point minimizing the sum of distances to a given set of points; Chebyshev center — the centre of the smallest ball containing a given set of points; In statistics: Iterated conditional modes — maximizing joint probability of Markov random field; Response surface methodology — used in the design of experiments