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Cross-Validation (Analysis Services - Data Mining) | Microsoft Docs
Cross-validation is a standard tool in analytics and is an important feature for helping you develop and fine-tune data mining models. You use. Keywords: Model evaluation, data mining, software patterns, force resolution map . . Validating Patterns and Pattern Language with FRMs. In order to be. Data Validation. Page 2. What is Data Mining? l Progress in digital data acquisition and storage technology mining are often referred to as models or patterns.
Validation is the process of assessing how well your mining models perform against real data. It is important that you validate your mining models by understanding their quality and characteristics before you deploy them into a production environment. This section introduces some basic concepts related to model quality, and describes the strategies for model validation that are provided in Microsoft Analysis Services.
For an overview of how model validation fits into the larger data mining process, see Data Mining Solutions. Use various measures of statistical validity to determine whether there are problems in the data or in the model. Ask business experts to review the results of the data mining model to determine whether the discovered patterns have meaning in the targeted business scenario. All of these methods are useful in mining methodology and are used iteratively as you create, test, and refine models to answer a specific problem.
No single comprehensive rule can tell you when a model is good enough, or when you have enough data. Measures of data mining generally fall into the categories of accuracy, reliability, and usefulness. Accuracy is a measure of how well the model correlates an outcome with the attributes in the data that has been provided.
There are various measures of accuracy, but all measures of accuracy are dependent on the data that is used. In reality, values might be missing or or the data might have been changed by multiple processes.
Particularly in the phase of exploration and development, you might decide to accept a certain 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 particular store based on past sales can be strongly correlated and very accurate, even if that store consistently used the wrong accounting method. Therefore, measurements of accuracy must be balanced by assessments of reliability.
Reliability assesses the way that a data mining model performs on different data sets. A data mining model is reliable if it generates the same type of predictions or finds the same general kinds of patterns regardless of the test data that is supplied.
For example, the model that you generate for the store that used the wrong accounting method would not generalize well to other stores, and therefore would not be reliable. Usefulness includes various metrics that tell you whether the model provides useful information.
For example, a data mining model that correlates store location with sales might be both accurate and reliable, but might not be useful, because you cannot generalize that result by adding more stores at the same location. Moreover, it does not answer the fundamental business question of why certain locations have more sales. You might also find that a model that appears successful in fact is meaningless, because it is based on cross-correlations in the data.
Analysis Services supports multiple approaches to validation of data mining solutions, supporting all phases of the data mining test methodology. Measuring lift and gain. A lift chart is a method of visualizing the improvement that you get from using a data mining model, when you compare it to random guessing. These charts sort good and bad guesses into a table so that you can quickly and easily gauge how accurately the model predicts the target value. Creating profit charts that associate financial gain or costs with the use of a mining model, so that you can assess the value of the recommendations.
Validating data mining models not aim to answer the question of whether the data mining model answers your business question; rather, these metrics provide objective measurements that you can use to assess the reliability of your data for predictive analytics, and to guide Validating data mining models decision of whether to use a particular iterate on the development process.
The topics in this section provide an overview of each method and walk you through the process of measuring the accuracy of models that you build using SQL Server Data Mining.
Methods for Testing and Validation of Data Mining Models There are many approaches for assessing the quality and Validating data mining models of a data mining model. Separate the data into Validating data mining models and testing sets to test the accuracy of predictions. Ask business experts to review the results of the data mining model to determine whether the discovered patterns have meaning in the targeted business scenario All of these methods are useful in Validating data mining models mining methodology and are used iteratively as you create, test, and refine models to answer a specific problem.
Definition of Criteria for Validating Data Mining Models Measures of data mining generally fall into the categories of accuracy, reliability, and usefulness.
Tools for Testing and Validation of Mining Models Analysis Services supports multiple approaches to validation of data Validating data mining models solutions, supporting all phases of the data mining test methodology. Partitioning data into testing and training sets.
Filtering models to train and test different combinations of the same source data. Performing cross-validation of data sets Generating classification matrices. Validating data mining models scatter plots to assess the fit of a regression formula.
Training and Testing Data Sets. Learn how to create a Validating data mining models matrix, sometimes called a confusion matrix, for assessing the of true and false positives and negatives.
Cross-validation is a standard carve in analytics and is an important feature as a replacement for helping you develop and fine-tune data mining models. You use cross-validation after you have created a mining structure and enmeshed mining models to ascertain the validity of the model.
Cross-validation has the following applications:. This component describes how to purpose the cross-validation features provided for data mining, and how to interpret the results of cross-validation fit either a single unequalled or for multiple models based on a simple data set. Cross-validation consists of two phases, training and result generation.
These phases include the following steps:. You specify the models you want to test. This step is optional; you can ordeal just the mining systematize as well. Analysis Services returns a set of accuracy metrics for each fold in each produce, or for the figures set as a uninjured.
You can customize the way that cross-validation works to control the compute of cross-sections, the models that are tested, and the accuracy bar championing predictions.
- Topics, Links. Learn how to set up a testing data set using a wizard or DMX.
- Cross-validation is a standard tool in analytics and is an important feature for helping you develop and fine-tune data mining models. You use.
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- Model Validation and Verification of Data Mining from the Knowledge Workers. Productivity Approach. Asadallah Najafi. Islamic Azad University, Zanjan Branch, . Data Validation. Page 2. What is Data Mining? l Progress in digital data acquisition and storage technology mining are often referred to as models or patterns.
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What qualities build attraction with women?Cross-validation is a standard tool in analytics and is an important feature for helping you develop and fine-tune data mining models. You use. Data Validation. Page 2. What is Data Mining? l Progress in digital data acquisition and storage technology mining are often referred to as models or patterns..
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