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What is Data Mining Metrics?
Data mining is one of the forms of artificial intelligence that uses perception models, analytical models, and multiple algorithms to simulate the techniques of the human brain. Data mining supports machines to take human decisions and create human choices.
The user of the data mining tools will have to direct the machine rules, preferences, and even experiences to have decision support data mining metrics are as follows −
Usefulness − Usefulness involves several metrics that tell us whether the model provides useful data. For instance, a data mining model that correlates save the location with sales can be both accurate and reliable, but cannot be useful, because it cannot generalize that result by inserting more stores at the same location.
Furthermore, it does not answer the fundamental business question of why specific locations have more sales. It can also find that a model that appears successful is meaningless because it depends on cross-correlations in the data.
Return on Investment (ROI) − Data mining tools will find interesting patterns buried inside the data and develop predictive models. These models will have several measures for denoting how well they fit the records. It is not clear how to create a decision based on some of the measures reported as an element of data mining analyses.
Access Financial Information during Data Mining − The simplest way to frame decisions in financial terms is to augment the raw information that is generally mined to also contain financial data. Some organizations are investing and developing data warehouses, and data marts.
The design of a warehouse or mart contains considerations about the types of analyses and data needed for expected queries. It is designing warehouses in a way that allows access to financial information along with access to more typical data on product attributes, user profiles, etc. can be useful.
Converting Data Mining Metrics into Financial Terms − A general data mining metric is the measure of "Lift". Lift is a measure of what is achieved by using the specific model or pattern relative to a base rate in which the model is not used. High values mean much is achieved. It can seem then that one can simply create a decision based on Lift.
Accuracy − Accuracy is a measure of how well the model correlates results with the attributes in the data that has been supported. There are several measures of accuracy, but all measures of accuracy are dependent on the information that is used. In reality, values can be missing or approximate, or the data can have been changed by several processes.
It is the procedure of exploration and development, it can decide to accept a specific amount of error in the data, especially if the data is fairly uniform in its characteristics. For example, a model that predicts sales for a specific store based on past sales can be powerfully correlated and very accurate, even if that store consistently used the wrong accounting techniques. Thus, measurements of accuracy should be balanced by assessments of reliability.
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