Descriptive models use data aggregation and data mining to uncover patterns in past or current events. A familiar example of descriptive modeling is business reporting in the form of graphs, charts, and dashboards.
In unsupervised learning, the data mining algorithms describe some intrinsic property or structure of data and hence are sometimes called descriptive models.
Frequency Distribution
A data set is made up of a distribution of values, or scores.
In tables or graphs, you can summarize the frequency of every possible value of a variable in numbers or percentages.
This is called a frequency distribution.
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Measures of Central Tendency
Measures of central tendencyestimate the center, or average, of a data set.
The mean, median and mode are 3 ways of finding the average.
Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.
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Types of Descriptive Statistics
There are 3 main types of descriptive statistics:.
1) The distributionconcerns the frequency of each value.
2) The central tendency concerns the averages of the values.
3) The variability or dispersion concerns how spread out the values are.
You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more,.
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What is a descriptive model?
A descriptive model, on the other hand, is describing the data in a form that allows for future action strategies, but it is not a precise event.
Rather, it is a perspective into large quantities of data, so business can make sense of the data.
It describes data in clusters or association rules so it doesn’t need to be accurate, just approximate.
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What is descriptive statistics?
Revised on June 21, 2023.
Descriptive statistics summarize and organize characteristics of a data set.
A data set is a collection of responses or observations from a sample or entire population.
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What is the difference between a descriptive model and a clustering model?
In descriptive models, decision strategies are still needed to address the gray area introduced by descriptive models.
In case of outlier detection using clustering, the idea is to supply a large data set to a clustering algorithm and it will plot the data points and look for points that are close to form a cluster.
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What is the difference between descriptive model output and predictive analytics?
In predictive analytics, a future event is predicted and that has to be exploited favorably.
The focus is the event and, therefore, the usage is tied to the event as well.
In contrast, the descriptive model output is an explanation of the data using a structured form like clustering or social network analysis.
