Hi All,
We're currently preparing for a project for a bank client of ours where we would be using SQL Server 2008's data mining capabilities.
Does anyone know if logistic regression supports the following types:
Binomial (standard)
Multinomial (standard)
Conditional
Ordered
Rank-ordered
Nested
Stereotype
Regards,
Joseph
Hello,
This question is regarding the LogRegHelper - "A scorecard for Logistic Regression models" example in sqlserverdatamining Tips and Tricks page. I launched TestLogReg (Analysis Services Database associated with the project) and ran Logistic Regression over that. While the LogReg shows the highest score for IQ (107 - 121), a score of 558, the Logistic Regression shows that Parent Encouragement has the highest score for the case College Plans = 'Plans to Attend'. Can someone verify this and clarify?
I have a few other questions with LR
- In SQL Server 2005 LR Mining Model Viewer "favors" chart, what algorithm is used for generating Scores?
- Can I use this score as a feature selector? Higher score => stronger predictor (input)
- Is the coefficient weight algorithm used in LogReg wrong ?
Thanks
MA
Hi!
I bought the book €œData Mining with SQL Server 2005€?, but I can€™t find the solution to a problem I have.
I want to retrieve from C# the logistic regression Attribute Value (AV) Scores for the Logistic Regression Algorithm. I can see the Scores from the Microsoft Logistic Regression Viewer (the same of Neural Network Viewer), but I cannot retrieve them via DMX, OLEDB or similar.
Otherwise, is there a formula that I can use to compute that score from the coefficient, support, or probability values of the Attribute Value pair (I can read this values from DMX)?
I can access to them via DMX:
NODE_DISTRIBUTION -> SUPPORT and PROBABILITY ATTRIBUTE_VALUE...
with a query like
SELECT FLATTENED (SELECT ATTRIBUTE_NAME, ATTRIBUTE_VALUE FROM NODE_DISTRIBUTION WHERE VALUETYPE = ... ) FROM [MyModel].CONTENT WHERE NODE_TYPE ....
Thanks in advance
Regards,
Marco
I need to write some SQL to do a power regression for a trendline. I have 2 columns of data which represent my X, Y data and all I'm after is the a and the b for the function y=ax^b. Has anyone ran into this before?? I know SSAS has a linear regression function but my data really only fits the power model.
View 4 Replies View RelatedHi!
I try to make linear regression in multiple dimensions
with SSAS (y = a + a1*x1+ ... a2*xn)
I got the equation, but I also want to see R squared and R adjusted in same manner as in Excel.
How to achieve that?
Greetings
How do I write a DMX query to return the coefficients of the independent variables in my regression equation?
Thanks,
Carrie
I would know if is possible to add the regression line to a scatter chart !!!
View 5 Replies View Related[using: Reporting Services 2005, SQL Server 2005, Analysis Services 2005]
Has anyone ever implemented dynamic trendlines with RS charts?
I have a requirement to create a web-based chart based on an existing Excel chart that the client is already using. This chart uses a trendline to forecast performance for 3 months out. I know in Excel it's as easy as right-click->add trendline.
Is there a similarly simple way to do this in Reporting Services?
Also, the data source for this is OLAP, so if any of you are MDX gurus, is there some regression function to plot all the parallel axis points?
thanks for any insight.
-michael
This is a real challenge. I hope someone is smart enough to know howto do this.I have a tableTABLE1[Column 1- 2001][Column 2- 2002][Column 3- 2003][Column 4 - 2004][Column 5 - 2005][Column 6 - 2006][Column 7 - Slope][2001][2002][2003][2004][2005][2006] [Slope][1] [2] [3] [4] [5] [6] [1][1.2] [.9] [4] [5] [5.4] [6.2] [?]Slope is defined as "M" in the equation y=mx+bI need a way a finding the linear equation that best fits the points soI can have SQL calculate the slope.Are there any smart people around that would know how to do this?thanks
View 3 Replies View RelatedI would like to understand the algorithm that the linear regression method uses to choose the regressors in the model from a list of possible regressors.
I think that it is different from the common methods used in statistics like stepwise, forward or backward.
Laura Lerner
I have two questions about the regression tree of Microsoft Decision Trees algorithm.
1. The mining legend window has a column named Histogram showing a bar for each coefficient. What does this bar mean?
2. Since each node of a regression tree corresponds to a linear regression, how can I find the regression coefficient of each node? I mean the coefficient that tells how good the regression is.
Any tip will be greatly appreciated.
Hello,
I need to develop a Probit Regression Plug-In Algorithm.
Does anyone know if the plug-in framework will reasonably handle a Probit Regression?
Is anyone aware of any code or materials, specific to a Probit Regression Plug-in, that would help me to do this?
I am also interested in applying the dprobit methodology found in Stata for infinitesimal changes in independent variables.
Has anyone been successful using Stata to implement an SSAS plug-in algorithm?
thank you,
Bill Littlewood
Hi there,
We need to determine the prediction formula coefficients using the multivariate regression formula as is available in Excel AnalysisTool pack [something like Y = Ax + Bz + C and find A, B, C]. It would be a very "simple" type of analysis that would run on a single table. There does not seem to be an easy built-in SQL function to perform this. However, reading on the web, Analysis Services might be used to do this task? Is there a good sample for a multivariate regression?
Actually, is this a proper approach given the relative simplicity of the calculation? Do we really need to go through the trouble of setting up an Analysis Service solution just for this task?
Thanks in advance
We are trying to create a model of linear regression with nested table. We used the create mining model sintax as follow :
create mining model rate_plan3002_nested2
( CUST_cycle LONG KEY,
VOICE_CHARGES double CONTINUOUS predict,
DUR_PARTNER_GRP_1 double regressor CONTINUOUS ,
nested_taarif_time_3002 table
( CUST_cycle long CONTINUOUS,
TARIFF_TIME text key,
TARIFF_VOICE_DUR_ALL double regressor CONTINUOUS
)
) using microsoft_linear_regression
INSERT INTO MINING STRUCTURE [rate_plan3002_nested2_Structure]
(CUST_cycle ,
VOICE_CHARGES ,
DUR_PARTNER_GRP_1 ,
[nested_taarif_time_3002](SKIP,TARIFF_TIME ,TARIFF_VOICE_DUR_ALL)
)
SHAPE {
OPENQUERY([Cell],
'SELECT CUST_cycle ,
VOICE_CHARGES ,
DUR_PARTNER_GRP_1
FROM dbo.panel_anality_3002
order by CUST_cycle ')}
APPEND
({OPENQUERY([Cell],
'select CUST_cycle,
TARIFF_TIME,
CYCLE_DATE
from dbo.nested_taarif_time_3002
order by CUST_cycle,TARIFF_TIME')
}
relate CUST_cycle to CUST_cycle
) as nested_taarif_time_3002
The results we got are a model with intercept only. if we don't use the nested variable (the red line) we get a rigth model . (we had more variable ....)
Is there a way to do this regression correctly?
Thanks,
Dror
When using linear regression in the SQL Server 2005 Business IntelIigence Studio I interpet the information below as follow: X has a standard deviation of +- 37.046. Is it possible to obtain the standard deviation of each coefficient in the regression expression?
That solved the application problem
However, now we face a different challenge. Running the same data through the SSAS Linear Regression model and the Excel Regression [Data Analysis] tool we get different answers:
Intercept
-3.57537
x
0.242462
z
0.353668
SSAS:
Intercept -2.95188545928199
x 0.201587406861264
z 0.371940525462092
In Excel we set up the Regression analysis using the 95% confidence interval. Is there a concept for confidence interval for linear regression in SSAS?. Since we are doing this for a company that has been using Excel for years, I do not think such a difference in results will be accepted...
Is there anything else we can do to ensure the answers are close? Must we then have to work around and call these calculations from Excel?
Hi,
I am trying to create a model using microsoft Linear Regression algorithm. But I want to constrain the coefficient of the parameters to non-negative value. There is concept of bound in SAS where we can specify the range of the coefficient. Does any of the SSAS mining algorithms support restricting the coefficient value?
Thanks,
DMN
Q1. Model Prediction -- Suppose we already have a trained Microsoft Linear Regression Mining Model, say, target y regressed on two variables:
x1 and x2, where y, x1, x2 are of datatype Float. We try to perform Model Prediction with an Input Table in which some records consist of NULL x2 values. How are the resulting predicted y values calculated?
My guess:
The resulting linear regression formula is in the form:
y = constant + coeff1 * (x1 - avg_x1) + coeff2 * (x2 - avg_x2)
where avg_x1 is the average of x1 in the training set, and avg_x2 is the average of x2 in the training set (Correct?).
I guess that for some variable being NULL in the Input Table, Microsoft Linear Regression just treat it as the average of that variable in the training set.
So for x2 being NULL, the whole term coeff2 * (x2 - avg_x2) just disappear, as it is zero if we substitute x2 with its average value.
Is this correct?
Q2. Model Training -- Using the above example that y regressed on x1 and x2, if we have a train set that, say, consist of 100 records in which
y: no NULL value
x1: no NULL value
x2: 70 records out of 100 records are NULL
Can someone help explain the mathematical procedure or algorithm that produce coeff1 and coeff2?
In particular, how is the information in the "partial records" used in the regression to contribute to coeff1 and the constant, etc ?
Q1. Model Prediction -- Suppose we already have a trained Microsoft Linear Regression Mining Model, say, target y regressed on two variables:
x1 and x2, where y, x1, x2 are of datatype Float. We try to perform Model Prediction with an Input Table in which some records consist of NULL x2 values. How are the resulting predicted y values calculated?
My guess:
The resulting linear regression formula is in the form:
y = constant + coeff1 * (x1 - avg_x1) + coeff2 * (x2 - avg_x2)
where avg_x1 is the average of x1 in the training set, and avg_x2 is the average of x2 in the training set (Correct?).
I guess that for some variable being NULL in the Input Table, Microsoft Linear Regression just treat it as the average of that variable in the training set.
So for x2 being NULL, the whole term coeff2 * (x2 - avg_x2) just disappear, as it is zero if we substitute x2 with its average value.
Is this correct?
Q2. Model Training -- Using the above example that y regressed on x1 and x2, if we have a train set that, say, consist of 100 records in which
y: no NULL value
x1: no NULL value
x2: 70 records out of 100 records are NULL
Can soemone help explain the mathematical procedure or algorithm that produce coeff1 and coeff2?
In particular, how is the information in the "partial records" used in the regression to contribute to coeff1 and the constant, etc ?
We are seeing a regression bug with the Microsoft JDBC driver 1.2 CTP.
Using this driver, we don't seem to be able to call stored procedures which return a result set, if those stored procedures use temporary tables internally.
The 1.2 CTP driver fails to access such stored procedures in both SQL Server 2000 and SQL Server 2005 databases.
The previous 1.1 driver, suceeds in both cases.
Here is a test case which demonstrates the problem (with IP addresses and logins omitted). The prDummy stored procedure being called is quite simple, and I've copied it below:
Code Snippet
public class MicrosoftJDBCDriverCallingStoredProceduresTest extends TestCase {
// CREATE PROCEDURE [dbo].[prDummy]
// AS
//
// CREATE TABLE #MyTempTable (
// someid BIGINT NOT NULL PRIMARY KEY,
// userid BIGINT,
// )
//
// SELECT 1 as TEST2, 2 as TEST2
// GO
public void testStoredProcedureViaDirectJDBC() {
Connection conn = null;
String driverInfo = "<unknown>";
String dbInfo = "<unknown>";
try {
// Set up driver & DB login...
Class.forName("com.microsoft.sqlserver.jdbc.SQLServerDriver");
String connectionUrl = "jdbc:sqlserver://xxx.xxx.xxx.xxx:1433";
Properties dbProps = new Properties();
dbProps.put("databaseName", "xxxxxx");
dbProps.put("user", "xxxxxx");
dbProps.put("password", "xxxxxx");
// Get a connection...
conn = DriverManager.getConnection(connectionUrl, dbProps);
driverInfo = conn.getMetaData().getDriverName() + " v" + conn.getMetaData().getDriverVersion();
dbInfo = conn.getMetaData().getDatabaseProductName() + " v" + conn.getMetaData().getDatabaseProductVersion();
// Perform the test...
CallableStatement cs = conn.prepareCall("{CALL prDummy()}");
cs.executeQuery();
// If the previous line executes okay, the test is passed...
System.out.println("Accessing "" + dbInfo + "" with driver "" + driverInfo + "" calls the stored procedure successfully.");
}
catch (Exception e) {
// Fail the unit test...
fail("Accessing "" + dbInfo + "" with driver "" + driverInfo + "" fails to call the stored procedure: " + e.getMessage());
}
finally {
// Close the connection...
try { if (conn != null) conn.close(); } catch (Exception ignore) { }
}
}
}
The output of this test under both drivers and accessing both databases is as follows:
Code Snippet
Accessing "Microsoft SQL Server v8.00.2039" with driver "Microsoft SQL Server 2005 JDBC Driver v1.1.1501.101" calls the stored procedure successfully.
Accessing "Microsoft SQL Server v9.00.3042" with driver "Microsoft SQL Server 2005 JDBC Driver v1.1.1501.101" calls the stored procedure successfully.
Accessing "Microsoft SQL Server v8.00.2039" with driver "Microsoft SQL Server 2005 JDBC Driver v1.2.2323.101" fails to call the stored procedure: The statement did not return a result set.
Accessing "Microsoft SQL Server v9.00.3042" with driver "Microsoft SQL Server 2005 JDBC Driver v1.2.2323.101" fails to call the stored procedure: The statement did not return a result set.
How do I write a regression test for a stored proc that produces multiple rowsets via multipl e select queries? E.g.
CREATE PROCEDURE myProc AS
SELECT 'Some stuff', GETDATE()
SELECT 'Some more stuff'
For single-select procs, I can create a temp table and INSERT #temp EXEC myProc, then evaluate the contents of the table to verify correct behavior, but that doesn't work in this case.
With the number of threads it is difficult to know if this has been posted. If I use the Mining Content Viewer for Linear Regression, under Node Distribution, there are values given for Attribute Name, Attribute Value, Support, Probability, Variance, and Value Type. The output is similar to what Joris supplied in his thread about Predict Probability in Decision Trees. My questions:
1. How should these fields be interpreted?
2. With Linear Regression, is it possible to get the coefficient values and tests of significance (t-tests?), if they are not part of the output I have pointed to?
Thanks for your help with this?
Sam
In the 70-461 objectives it says: Ensure code non regression by keeping consistent signature for procedure, views and function (interfaces); security implications...I think I understand what this means in general. They want us to be able to create a view that will still be able to call the original data even if the table is modified. In other words, the view table shouldn't easily be broken. ie, type a code that does NOT ensure non regression, then change the code so that it does ensure non regression.Â
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