Data mining logistic regression

By | 06.01.2018
1

In Data Mining Designer, click the Mining Models tab. Click Create a related mining model. In the New Mining Model dialog box, for Model name, type Call Center - LR. For Algorithm name, select Microsoft Logistic Regression. Click OK. The new mining model is displayed in the Mining Models tab. Logistic regression estimate class probabilities directly using the logit transform. The Linear regression calculate a linear function and then a threshold in order to classify. The result is logistic regression, a popular classification technique. Logistic regression can be framed as minimizing a convex function but has no closed-form solution. Since we did not create a test partition when the data set was partitioned, the Score Test Data options are disabled. To create a test set, see the Data Mining Partition section. Click Finish. The logistic regression output worksheets are inserted to the right of the Data_Partition worksheet.
SQL SERVER DATA MINING. Data mining is the process of analyzing data and finding hidden patterns using automatic methodologies and the term has been used over. Logistic Regression for Data Mining and High-Dimensional Classification Paul Komarek Dept. of Math Sciences Carnegie Mellon University [email protected] In Data Mining Designer, click the Mining Models tab. Click Create a related mining model. In the New Mining Model dialog box, for Model name, type Call Center - LR. For Algorithm name, select Microsoft Logistic Regression. Click OK. The new mining model is displayed in the Mining Models tab. Logistic regression estimate class probabilities directly using the logit transform. The Linear regression calculate a linear function and then a threshold in order to classify. The result is logistic regression, a popular classification technique. Logistic regression can be framed as minimizing a convex function but has no closed-form solution. Since we did not create a test partition when the data set was partitioned, the Score Test Data options are disabled. To create a test set, see the Data Mining Partition section. Click Finish. The logistic regression output worksheets are inserted to the right of the Data_Partition worksheet.

Logistic Regression

Introduction

Often, the analyst is required to construct a model which estimates probabilities. This is common in many fields: medical diagnosis (probability of recovery, relapse, data mining logistic regression, etc.), credit scoring (probability of a loan being repaid), sports (probability of a team beating a competitor- wait. maybe that belongs in the "investment" category?).

Many data mining logistic regression are familiar with linear regression- why not just use that? There are several good reasons not to do this, but probably the most obvious is that linear models will always fall below 0.0 and poke out above 1.0, yielding answers which do not make sense as probabilities.

Many different classification dgb mining calculator have been devised which estimate the probability of class membership, such as linear and quadratic discriminant analysis, neural networks and tree induction. The technique covered in this article is logistic regression- one of the simplest modeling procedures.


Logistic Regression

Logistic regression is a member of the family of methods called generalized linear models ("GLM"). Such models include a linear part followed by some "link function". If you are familiar with neural networks, think of "transfer functions" or "squashing functions". So, the linear function of the predictor variables is calculated, and the result of this calculation is run through the link function. mining board bitcoin In the case of logistic regression, the linear result is run through a logistic function (see figure 1), which runs from 0.0 (at negative infinity), rises monotonically to 1.0 (at positive infinity). Along the way, it is 0.5 when the input value is exactly zero. Among other desirable properties, note that this logistic function only returns values between data mining logistic regression and 1.0. Other GLMs operate similarly, but employ different link functions- some of which are also bound by 0.0 - 1.0, and some of which are not.



Figure 1: The Most Interesting Part of the Logistic Function (Click figure to enlarge)


While calculating the optimal coefficients of a least-squares linear regression has a direct, closed-form solution, this is not the case for logistic regression, data mining logistic regression. Instead, some iterative fitting procedure is needed, in which successive "guesses" at the right coefficients are incrementally improved. Again, if you are familiar with neural networks, this is much like the various training rules used with the simplest "single neuron" models. Hopefully, you are lucky enough to have a routine handy to perform this process for you, such as glmfit, from the Statistics Toolbox.


glmfit

The glmfit function is easy to apply. The syntax for logistic regression is:

B = glmfit(X, [Y N], 'binomial', 'link', 'logit');

B will contain the discovered coefficients for the linear portion of the logistic regression data mining logistic regression link function has no coefficients). X contains the pedictor data, with examples in rows, variables in columns. Y contains the target variable, usually a 0 or a 1 representing the outcome. Last, the variable N contains the count of events for each row of the example data- most often, this will be a columns of 1s, the same size as Y. The count parameter, N, will be set to values greater than 1 for grouped data. As data mining logistic regression example, think of medical cases summarized by country: each country will have averaged input values, an outcome which is a rate (between 0.0 and 1.0), and the count of cases from that country. In the event that the counts are greater than one, then the target variable represents the count of target class observations.

Here is a very small example:

>> X = [0.0 0.1 0.7 1.0 1.1 1.3 1.4 1.7 2.1 2.2]';
>> Y = [0 0 1 0 0 0 1 1 1 1]';
>> B = glmfit(X, [Y ones(10,1)], 'binomial', 'link', 'logit')

B =

-3.4932
2.9402


The first element of B is the constant term, and the second element is the coefficient for the lone input variable. We apply the linear part of this logistic regression thus:

>> Z = B(1) + X * (B(2))

Z =

-3.4932
-3.1992
-1.4350
-0.5530
-0.2589
0.3291
0.6231
1.5052
2.6813
2.9753


To finish, we apply the logistic function to the output of the linear part:

>> Z = Logistic(B(1) + X * (B(2)))

Z =

0.0295
0.0392
0.1923
0.3652
0.4356
0.5815
0.6509
0.8183
0.9359
0.9514


Despite the simplicity of the logistic function, I built it into a small function, Logistic, so that I wouldn't have to repeatedly write out the formula:

% Logistic: calculates the logistic function of the input
% by Will Dwinnell
%
% Last modified: Sep-02-2006

function Output = Logistic(Input)

Output = 1 ./ (1 + exp(-Input));


% EOF



Conclusion

Though it is structurally very simple, logistic regression still finds wide use today in many fields. It is quick to fit, easy to implement the discovered model and quick to recall. Frequently, it yields data mining logistic regression performance than competing, more complex techniques. I recently built a logistic regression model which beat out a neural network, data mining logistic regression, decision trees and two types of discriminant analysis. If nothing else, it is worth fitting a simple model such as logistic regression early in a modeling project, just to establish a performance benchmark for the project.

Logistic regression is closely related to another GLM procedure, probit regression, which differs only in its link function (specified in glmfit by replacing 'logit' with 'probit'). I believe that probit regression metals and mining industry analysis been losing popularity since its results are typically very similar to those from logistic regression, but the formula for the logistic link function is simpler than that of the probit link function.



References

Generalized Linear Models, by McCullagh and Nelder (ISBN-13: 978-0412317606)


See Also

The Apr-21-2007 posting, data mining logistic regression, Linear Regression in MATLAB, the Feb-16-2010 posting, Single Neuron Training: The Delta Rule and the Dec-11-2010 posting, Linear Discriminant Analysis (LDA).
Источник:




Data Mining in MATLAB: Logistic Regression

Since we did not create a test partition when the data set was partitioned, the Score Test Data options are disabled. To create a test set, see the Data Mining Partition section. Click Finish. The logistic regression output worksheets are inserted to the right of the Data_Partition worksheet. Mar 15, 2009 · Introduction Often, the analyst is required to construct a model which estimates probabilities. This is common in many fields: medical diagnosis. This is an introduction to the SQL Server Microsoft Logistic Regression Data Mining Algorithm. The Laboratory for Advanced Computing develops technologies for high performance computing, high performance networking, internet computing, data mining and related. Regression is a data mining technique used to predict a range of numeric values (also called continuous values), given a particular dataset. For example, regression might be . Select the Microsoft Logistic Regression as the data mining technique. Please note in the description that the "algorithm is a particular configuration of the Microsoft Neural .

Logistic Regression for Data Mining and High-Dimensional Classification Paul Komarek Dept. of Math Sciences Carnegie Mellon University [email protected] Summary •Logistic Regression is a classification method. •It returns the probability that the binary dependent variable may be predicted from the independent. In Data Mining Designer, click the Mining Models tab. Click Create a related mining model. In the New Mining Model dialog box, for Model name, type Call Center - LR. For Algorithm name, select Microsoft Logistic Regression. Click OK. The new mining model is displayed in the Mining Models tab.


By: Dallas Snider   |   Read Comments   |   Related Tips: > Analysis Services Development



Problem

How can I set up a project to use the SQL Server Analysis Services logistic regression data mining algorithm?

Solution

In this tip, we show how to create a simple data mining model using the Logistic Regression algorithm in SQL Server Analysis Services. The data set we will use is visualized below. We are trying to classify the false samples in red and the true samples in blue. This tip uses SQL Server 2014 Analysis Services and Visual Studio 2012.

The data is stored in the table whose structure is shown below. We have a primary key column, along with columns for our X and Y values. The last column is where we store the class label.

In Visual Studio, create a new Analysis Services Multidimensional and Data Mining Project.

In this tip, we will name the project LogisticRegressionExample. Click on OK when finished with the New Project window.

In the Solution Explorer window, right-click on the Data Sources folder and choose "New Data Source..." to initiate the Data Source Wizard.

Click on "Next >".

Choose your data connection, if one exists. If a data connection does not exist, click on "New..." to create a new data connection.

In this example, we are using a connection to the Tips database on the localhost.

Click on "Next >".

On the Impersonation Information screen, click on "Use a specific Windows user name and password." Enter your username and password. Click on "Next >".

On the Completing the Wizard screen, the data source name can be changed if desired. Click on "Finish".

The new data source will appear in the Solution Explorer.

In the Solution Explorer window, right-click on the Data Source Views folder and choose "New Data Source View..." to launch the Data Source View Wizard.

Click on "Next >".

On the Select a Data Source page in the Relational data sources window, select the data source we created in the above step. Click on "Next >".

On the Select Tables and Views page, move the table tblLogisticRegressionExample from the Available Objects box to the Included object box by selecting tblLogisticRegressionExample in the Available objects box and then clicking on the ">" box. Click on "Next >".

On the Completing the Wizard page, give the Data Source View a name and click on "Finish".

The data source view now appears in the Solution Explorer window.

Right-click on the Mining Structures folder and select "New Mining Structure..." to launch the Data Mining Wizard.

Click on "Next >".

Press the "From existing relational database or data warehouse" radio button and then click "Next >".

Select the Microsoft Logistic Regression as the data mining technique. Please note in the description that the "algorithm is a particular configuration of the Microsoft Neural Network algorithm." This will become important later when it is time to view the results.

On the Select Data Source View page, we will use our previously defined objects. Click on "Next >".

Next, check the Case box on the ColumnsForDataMining line. Click on "Next >".

On the Specify the Training Data page, check the box in the Key column that corresponds with the primary key column. The AttributeX and AttributeY columns will be used as input. The BinaryClass column is our class label, so we check the Predictable box for the BinaryClass column. Click on "Next >".

The default values are shown below on the Specify Columns' Content and Data Type page. The values displayed in the Content Type and Data Type columns accurately represent the data in the source table. In this case, there is no need to click the Detect button. Click on "Next >."

We will use 30% of our data for testing the mining model's accuracy. Click on "Next >".

On the Completing the Wizard screen, we can rename the mining structure name and the mining model name. Click on "Finish".

Our mining structure now appears in the Solution Explorer.

The Mining Structure tab is selected by default. At this point the Analysis Services objects reside in the Visual Studio project and not on the server. Click on the Mining Model Viewer tab.

Visual Studio will attempt to deploy the SSAS objects to the server specified in the project properties. When asked if "Would you like to build and deploy the project first?", choose "Yes".

When given the warning about the time it could take to process the mining model and asked "Do you wish to continue?", choose "Yes". The number of records in the table is not a large amount, so it should not take more than a minute to process.

When the Process Mining Model window appears, press the "Run..." button.

The Process Progress window will appear. When the process completes successfully select "Close" in the Process Progress window and "Close" again in the Process Mining Model window.

Depending on your hardware configuration, the Load Mining Model Content window might appear stating to "Please wait...".

The Deployment Progress window will appear also stating that the SSAS objects were successfully deployed to the Analysis Services server.

In the Mining Model Viewer tab, we can see which attributes and their values favor the False classification and which attributes and their values favor the True classification. The wider the blue bar indicates the higher likelihood that a specific key-value pair will favor a particular classification. In the example shown here, when AttributeX is 0.684 to 1.000 the classification tends to be False. When AttributeX is between 0.000 to 0.298, then the classification tends to be True. The Logistic Regression algorithm utilizes the Microsoft Neural Network Viewer.

When we click on the Mining Accuracy Chart and then click on the Classification Matrix page, we can see the confusion matrix for the Logistic Regression algorithm. This displays the count of true positives, true negatives, false positives and false negatives in the 30% test population.

Summary

In this tip, we have provided an introduction to the Logistic Regression data mining algorithm in SQL Server 2014 Analysis Services.

Next Steps

Check out these other tips on data mining in SQL Server Analysis Services.



Last Update: 2015-01-29






About the author




Dr. Dallas Snider is an Assistant Professor in the Computer Science Department at the University of West Florida and has 18+ years of SQL experience.

View all my tips
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