Hi ,
I am a novice Data Mining Programmer.
I am using Time series algorithm for forecasting.
We are Quite concerned about the accuracy of Prediction output.
For Example Our Data is like this
If I write my Prediction Query to predict for 100 th time step.Its giving me out put like
Date Perf
03/01/2015 47.000000115
We are not sure about the accuracy of the values. Is it possible to use trend information as input to my model and make my prediction based on that.
I don€™t know how to do that? Can anyone help?
I am using time series agorithm.I need standard deviation in %. I am using SELECT StudID, PREDICTSTDEV([Perf]) FROM [Stud_Model].This one is giving me the standard deviation like this
Hi, I am not getting Mining Accuracy Chart and Min ing Model Prediction Plz tel me how to do.And how to use the filter input data used to generate the lift chart and select predictable mining model columns to show in the lift chart
Hi After building a model in BI, I want to view the chart of model in mining model viewer, in the chart tab I can just see one prediction value that means for my model do prediction for some time slice and in prediction steps I can specify how many steps, I want to show this chart In mining model viewer tab we can see the chart of prediction also decision tree and the chart is for showing all of value prediction, and with choosing prediction steps we can specify that show just one value prediction or two or several values. But sometime I can see just one value in chart and sometime I can see several values in chart, This difference is for my data or no? And also for viewing historic prediction I should choice €œshow historic prediction€? and before that I should set Two parameters: Historic_ model _count and historic _model _count, But I can€™t see historic prediction (sometime this happens) Please help me.
So sorry for posting yet another one of my silly posts about the correct way of doing things!
I am using Stored Procedures to do various things in my database (inserting/deleting/updating) and using SQL Server 2000 SP4
The current situation is that I have a stored procedure which checks certain user details and returns either -1 or 1 as a success indicator to the caller.
Now, obviously if we executed several queries in this stored procedure, performance will be an issue.
However, I have no idea how to balance up the whole "more useful information" vs "general information". What I mean is this:
if we are checking 3 items in a table, I want to return the success value back to the caller - in this case, we could do an IF statement to see if the record exists, if it does, set the return value to 1, else, -1.
But then you may want to be more informative to the user, specifically stating what part of the information they entered is incorrect/invalid. So having this in mind, we would then need to execute, say, 3 queries to return a more specific "error" value.
What should I do in this case?
I want to check the username, password and if the account is activated.
Currently I have this query going on:
IF EXISTS (SELECT [ID] FROM Users WHERE username = @un AND [password] = @pw AND activate = 1)
SET @theResult = 1
ELSE
SET @theResult = -1
firstly, is that the good way of doing things? if not - then what is the better way?
secondly, if we decide that we want a specific detailed return value/error message, it would mean I have to check the results I want using a couple more queries, such as one query to check username, one query to check password and another to check if the account is active or not and then return the appropriate value back to the caller.
is this a good way of doing things? I am confused and stuck!
Hi All I have installed SP2 for SQL server 2005, i just want to try datamining like in the tutorial, i found error if i click "Mining Accuracy Chart" for comparing model with error
"TITLE: Microsoft Visual Studio An error prevented the view from loading. ADDITIONAL INFORMATION: Class not registered (Exception from HRESULT: 0x80040154 (REGDB_E_CLASSNOTREG)) (System.Windows.Forms) " any body can help.
I've created models with Decision Tree and Neural Network algorithms that predict continous target. But I don't know how to interpret scores that occure under scatter accuracy plot. How should I interpret scores under scatter accuracy plot? How can I estimate occuracy of model created with Time Series? How can I compare accuracy of model created with Time Series with models created with Decision Trees and Neural Network algorithms?
I am given a task to test a complex stored procedure that has been recently modified for performance. The stored procedure runs 2 processes based on different parameters provided to it in real time. The procedure has been modified to incresease its performance from 22hours to just 1 minute. One of the two processes that this SP handels have been tested and is thus very accurate. The testing of 2nd process is given to me and I am looking for some great help in how to go about testing it step by step. I can provide more info on the SP and how we increased its performance, if you are interested. I know its a lot to ask but I really need some help. I have limited knowledge in SQL.
I ran a decision tree, clustering and neural network mining model across a dataset of about 200,000 records. I am trying to evaluate the accuracy of each of my models but I can't view the results.
I get the following error:
Failed to execute the query due to the following error:
XML for Analysis parser: The XML for Analysis request timed out before it was completed. Execution of the managed stored procedure GenerateLiftTableUsingDatasource failed with the following error: Exception has been thrown by the target of an invocation.Microsoft::AnalysisServices::AdomdServer::AdomdException.
I have a problem getting information about accuracy (percentage of the right predictions) of the model using DMX. Is it possible to get information about accuracy of the model using DMX? I didn't find any useful function... My second idea was to build and process the model. And then compare states of the predictable columns of the test data to states that the model predicts on the same data. And count them. That would be the way to get percantage of the right predictions... The problem is that usage of the function COUNT is not allowed??? I tried:SELECT COUNT(*) FROM [My Model Name].Cases and it didn't work like in standard SQL... Is it possible to count rows in DMX? Any idea how to get accuracy (percentage) of the model? I would need this information in my application... Thanx for any idea, Ziga
In the Mining Accuracy Chart, the predictable columns of nested tables does not show up in the "Select predictable mining model columns to show in the lift chart" table. The "Predictable column name" is empty.
Predictable columns in the case table shows up, but not the predictable columns in the nested table. What am I missing?
Well thanx a lot because i've got the reply. And now i want to ask about prediction data mining using DTS. Should i make some code to perform in my vb application? and how to join my table case with the tree result?
I just found that I am not able to view the accuracy chart for my mining model. The error message is: no mining models are selected for comparision. Which is quite strange.
Where I am trying to find out the associations between various service activities so that when a customer buys a service activity we can recommend him/her others
I want to add a column to a table that contains the predicted value according to a decision tree mining model. (I know that this is possible). But now I would like that when a new row is added to this table, and every column except the prediction column is filled in manually, can ms sql server add the predicted value automatically for this row? I know it is possible to execute a Singleton query for this kind of single prediction, but I would like to integrate this in my data table, because for now my steps would be: - Create the table with one prediction column - Add the known values of all columns for one row - Use singleton query in Mining model prediction tab to know the predicted value - Fill in the predicted value manually in my table.
i have mining model with 20 columns; 10 columns are for data (A1,A2...A10) and 10 columns are for prediction (B1,B2...B10) data is not in nest table, just one table using Association Rules A1 text A2 text ... A10 text
B1 text prediction only B2 text prediction only ... B10 text prediction only
i have rules as form Ai-->Bj.
i want to make a statement to prediction Bj values when i have Ai values, with Ai get from some textbox on screen, Can you show me some Examples.
I get to page 116 in "Data Mining with SQL Server 2005", but when I try to deploy my model, I get these errors:
Error 1 Error (Data mining): The 'Movie Bayes' mining model cannot have more than one predictable attribute. 0 0
Error 2 Errors related to feature availability and configuration: The 'Multiple prediction targets' feature is not included in the 'Standard Edition' SKU. 0 0
Hi All, I was wondering if there was a way to specify a range when training a model to predict continuous variables. For instance, the predicted variable can only have a range of 1 - 10.
I have tree models trained and now how do I perform predictions? I've read that i can use ADO in my vb language so could anyone give me some code lines about prediction using ADO and Vb language? and how to view the prediction in my vb application?
I have a market basket model using associations. It generated several dozen itemsets. However when I attempt to run a singleton prediction like this:
select (Predict(Orderproduct3q,INCLUDE_STATISTICS,10)) as [Recommendation]
From
[Case All]
NATURAL PREDICTION JOIN
(SELECT (SELECT '16407' AS [Pname])) AS t1
the resulting predictions don't take the itemsets into account. Instead, the predictions consist of the ranked products in the training set, ordered by frequency. This appears to happen regardless of the precise query specified within the "natural prediction join".
What's going on here and how do I generate a singleton prediction which makes use of the itemsets?
I'm building a mining model wiht MS Association Rules. After processing this model, the result includes some rules(example):
E = Existing, C = Existing -> B = Existing F = Existing -> E = Existing C = Existing, B = Existing -> E = Existing F = Existing -> B = Existing B = Existing, A = Existing -> C = Existing F = Existing, B = Existing -> E = Existing F = Existing, E = Existing -> B = Existing D = Existing -> A = Existing C = Existing -> A = Existing E = Existing, A = Existing -> B = Existing
I want to buid a query that has two or more items on the left of the rules, example: E = Existing, C = Existing -> B = Existing ->I want to buid a query to predict that: when a customer buy 'E' and 'C' then he likely buys 'B'
If your prediction join is to a SQL datasource, you can easily write a SQL query which returns a nested table like:
SELECT Predict([Subcategories],2) as [Subcategories] FROM [SubcategoryAssociations] NATURAL PREDICTION JOIN (SELECT (SELECT 'Road Bikes' AS Subcategory UNION SELECT 'Jerseys' AS Subcategory ) AS Subcategories ) AS t
What about if your datasource is a cube? Is there some special MDX syntax similar to the SQL syntax above? Or do you have to utilize the SHAPE/APPEND syntax as follows?
SELECT t.*, $Cluster as ClusterName FROM [MyModel] PREDICTION JOIN SHAPE { select [Measures].[My Measure] on 0, [My Dimension].[My Attribute].[My Attribute].Members on 1 from MyCube } APPEND ( { select [Measures].[Another Measure] on 0, NON EMPTY [My Dimension].[My Attribute].[My Attribute].Members *[Product].[Product].[Product].Members on 1 from MyCube } RELATE [[My Dimension]].[My Attribute]].[My Attribute]].[MEMBER_CAPTION]]] TO [[My Dimension]].[My Attribute]].[My Attribute]].[MEMBER_CAPTION]]] ) AS [My Nested Table] AS t ON [MyModel].[Product].[Product] = t.[My Nested Table].[[Product]].[Product]].[Product]].[MEMBER_CAPTION]]]
Hi I made an model based on MS time series and want to see some result in Mining Model Viewer (SSAS 2005) The chart displayed under tab "Charts" is as expected, but when i increase "predictions steps" onlsy the shadowed part of chart becomes bigger, but the prediction curve behaves unchanged...
I am trying to use the time series algorithm to predict responses to promotion mailings for subscription renewals. The problem i am having is that response is largely influenced by the number of mailings that are sent out. Can anyone give me any ideas on how i can structure the dataset so that it would take into account how many promotions were sent out? any help would be greatly appreciated.
Is it possible to use two algorithms together?I need to write prediction Query so that its should both models having clustereing algorithm and timeseries algorithm.
for example
I am having student information.I ve to predict performance of students for certain period.The students should be classified by their types like rich kids,poorkids..like that.I need to predict the performance of the rich kids??
Dear friends, I'm reading Wiley's Data mining with SQL Server 2005... There are MANY things I can't understand about MovieClick example (Chapter 3). I hope someone is going to help me with this troubles...
WARNING (1): I'm a dummy both with sql server and data mining. WARNING (2): My English is not good at all.
Just two questions for now:
1) When I create the model to predict the number of bedrooms for homeowners, the book says to check BEDROOMS as Predictable... question: is it also an INPUT for the model, or PREDICTABLE only?
2) I'd like to keep this model (number of bedrooms.......) and make a prediction query.
- Query builder - select case table -> Homeowners - Drag the Customer ID column from the Homeowners table and drop it on the grid - Drag the BEDROOMS column from the mining model and drop it on the grid. - On the last row: Source=PredictionFunction, Field=PredictProbability - Drag the BEDROOMS column from the mining model and drop it into Criteria/Argument - Add (i.e.) 'Two or Three' to the field Criteria/Argument
I execute the query and I obtain many rows in a table with the following colums: CustomerID, BEDROOMS and Expression: WHAT DOES THIS MEAN? WHICH INFO DO I GET FROM THOSE NUMBERS? WHAT CAN I LEARN FROM THEM?
I am doing this right now this way: 1) I do the DMX prediction query where I get the PredictNodeId(predict_var), my query is like this:
SELECT PredictNodeId(predict_var), model_1.predict_var, t.var_1, t.var_2 FROM model_1 PREDICTION JOIN OPENQUERY([DATA_SOURCE_1], 'SELECT var_1, var_2 FROM table_1') AS t ON model_1.var_1 = t.var_1 AND model_1.var_2 = t.var_2 2)I do the DMX query to get the node_description from the model.content iterating each row from the result of my prediction query, this query is like this:
SELECT node_description FROM model_1.content WHERE node_name = 'node_name_var'
In this query node_name_var = PredictNodeId(predict_var) from my prediction query. What I want to know if there is a way to merge Query 1 and Query 2 so I can get the node_description in the same query qhere I get the PredictNodeId.
Can i use a CASE statement in a prediction query. the following query is throwing me an error
SELECT CASE [Sales Forecast Time Series].[City Code] when 'LA' then 'Los Angeles' WHEN 'CA' THEN 'California' ELSE 'OTHERS' END, PredictTimeSeries([Sales Forecast Time Series].[Sales Value],5) From [Sales Forecast Time Series]
ERROR: Parser: The statement dialect could not be resolved due to ambiguity.
Also
Is it possible to discretize the Sales Value column using a the CASE statement, the output column of PredictTimeSeries function.
Is there a link that can give me a comprehensive info on what can be achieved and what cant be using DMX queries
Is there a way to display the actual predicted value for an output attribute for a particular model. For example, say I am trying to predict if a particular customer is going to take advantage of a promotion (0=no, 1=yes) and I use neural networks. I know that I can use "Predict" to give me the prediction "yes" or "no" for each customer. However, the neural network actually spits out a number as a result. For example, a 0.997 would be interpreted as a "yes" for life insurance promotion. I do not want the probability that the prediction is correct. I want the actual output for the network.
The reason being is that I want to compute an error rate between the predicted value and the acutal value (root mean squared error or some other measure). Is there a way to compute this using the mining model prediction tab design view? I do not want to write the actual query as I teach a course in data mining using SQL Server and my students do not know DMX queries.
