Hi, for a new project i'm trying to build a tree structure in SQL using one table with 'Node' & 'ParentNode' fields along with 'title', etc.
Table = Tree Node : ParentNode : Title : Show_Record 1 0 Root 1 2 1 Child 1
Then i'm trying to get SQL to return that in XML to my Tree Control 'oBout ASP TreeView'.
Now the tree control can accept XML fine as long as it's in a set format, which shouldn't be difficult and should cut my code from 200 lines to one.
However getting SQL to return the table records in XML is proving to be a total nightmare.
I've hunted the web but not getting very far, I've even got a couple of O'Reilly guides but still no luck, so any help would be excellent with this.
I wrote a sql query (basic 'select * from tree for xml raw') which returns the results in RAW XML, but when I run this in Query Analyser it returns the results as one long string broken up with '<' & '>' but gets to the third record and cuts off halfway.
I am studying the behavior of 200.000 clients. With the use of decision trees I would like to know if my clients will abandon our service or not. I use a training set of 21.822 clients and I use a predict variable "aband" wich is a discrete variable and it can be 0 or 1. In my training set i have 21.597 cases in which aband is 0 and 255 cases in which aband is 1. Looking at the classification matrix obtained using as input table a testing set (unselected data) I can see that my decision tree doesn't recognize the cases in which aband is 1. Here is the Classification Matrix: Counts for Dati Training on [Aband] Predicted 0 (Actual) 1 (Actual) 0 21597 225 1 0 0
Hi! I have created a DMM using Trees. But when I go to the Mining Model Predition tab and select a Predict function, I get this in the criteria column: <Scalar column reference>[, EXCLUDE_NULL|INCLUDE_NULL][, INCLUDE_NODE_ID]. When select Result, I get this error: "An incorrect number of arguments are used in the function at line 3, column 3." I'm predicting a continuous variable.
But when I delete everything except <Scalar column reference> I get this error: "Parser: The syntax for '<' is incorrect."
When I delete everything in the criteria column, I get this: "Query execution failed."
If I change the criteria to "<Scalar column reference>,INCLUDE_NULL, INCLUDE_NODE_ID" I get the error again that the query execution failed.
I'm working from a data set I created. I had no problems with predictions using clustering, but can't seem to get Trees to work.
I would appreciate answers to the following doubts I have regarding Decision trees, CONTAINS and using CONTAINS in a DMX query:
1. Does MS decision tree work only off equality/inequality conditions for the nodes? Is it possible to use a predicate as the branch criteria for a node?
2. Can the T-SQL predicate CONTAINS(...) be used in a DMX query? I need to check if a column-value is a substring of another column and create an intermediate column that will enable me to construct a decision tree with the phrase-present/absent branch.
3. Can CONTAINS(...) be used in a select clause? Like -
SELECT CONTAINS(JAT.column1, '"Good day"')
FROM JustAnotherTable;
4. Does CONTAINS(...) support both arguments to be column references? Or, is it mandatory that the pattern (argument #2) has to be a literal string or a variable? E.g.: I need to know the validity of the following expression -
I'm new to data mining, and have created an MS decision trees model. The model has the columns age, call outcome, call reason, country name, employee name and gender - all as inputs.
In the mining model viewer, I only get nodes for the age, despite having data for all the other columns.
I am trying to build a decision tree to predict prices. I have created the tree and looked at the lift charts, but I have not seen any of the traditional statistics I am used to from other programs (R-Squared, F statistics, etc.).
Does anyone have an example of how they calculated R-Squared for a decision tree on a continuous variable?
I installed the bike buyer example and i am learning the DMX language. Now i wrote the following query (using MS decision trees):
SELECT T.[Last Name], [Bike Buyer], PredictProbability(Predict([Bike Buyer])) AS [Probability] From [v Target Mail] PREDICTION JOIN OPENQUERY (....... And so on..)
Now the result is surprising to me. In the resulttabel all the probabilities are equal.
Bike Buyer Probability 1 0.99994590500919611 0 0.99994590500919611 0 0.99994590500919611 0 0.99994590500919611 0 0.99994590500919611 1 0.99994590500919611
and so on.
Now i am wondering what predictProbability means. I thought that PredictProbability meant the probability that the prediction is correct. Now all the probabilities are the same and the input is different. Can somebody tell me what PredictProbability means or am I using it wrong?
In a decision tree algorithm, is there a known way to force a branch at a top level? For exmaple, I have 30 known decision patterns that are going to be completely different and I don't want them to intermingle. I wanted to force a branch at the top node on one of the 30 patterns so I wouldn't have to create 30 mining models per client.
I have some accounting data, with some transaction attributes and amounts. I'm using Decision Trees to try and predict the next month's amount for certain combinations of attributes.
I've tried two different structures for the model:
A: one with 9 discrete text input attributes. B: And another with the same 9 attributes + a avarage Amount for all combinations of the nine attribute for every transaction.
When i've processed them and look in the dependency network, it says that the strongest link for the structure A is attribute "1". And for the second its the avarage-Amount attribute. Okey, that seems fine, but the second strongest link in structure B is attribute "2".
Shouldn't it be attribute 1 like in structure A?
Second question, if I run the same data in a Neural Network model, the prediction becomes much worst then the decision tree. I get many predictions that are negative values even though all training data contains positiv values. The StDev becomes the same for every row also.. What am I doing wrong with that one. I have alot of transactions and a read somewhere that a Neural Network should work better than a decision tree in a case similar to mine. The score in the "Lift chart" for the Neural Network model becomes 0,00 and for Decision Trees with the same data I get around 110.
I am using MS Decision Trees algorithm and for a specific model i get the above warning.As a result of that i dont get any splits in my tree. Is there anything i can do to avoid this?
I am trying to run one of the mining models from the book "Delivering BI using SQl Server 2005" but I am running into "Decision Trees found no splits for model". The mining structure has 4 columns, the fourth one being marked as "Predict Only". My Cube slice for the model has sufficient data in the cube. I am lost.. Help!!
While recently working with several mining models, I came across something that struck me as pretty odd - and I'm hoping to find an explanation for the behavior.
Consider the following setup:
A single table in the relational database represents the only case table A single, continuous column is the predictable A mining structure has been created
The mining structure contains a single model, based on the MS Decision Trees algorithm Input columns were selected for the model via the BI Studio wizard (i.e., those provided via the "Suggest" button) The structure has been fully processed Now, the interesting parts:
I view the scatterplot for the mining model, under the Mining Accuracy Chart tab Back on the Mining Structure tab, I delete one of the input columns I add the same column back into the structure The structure is fully processed again When I view the scatterplot for the mining model, under the Mining Accuracy Chart tab, a different set of data points are presented for the model predictions A different set of decision trees under the Mining Model Viewer tab confirms thisHow could different patterns have been found this second time around, even though all of the input columns were the same (as well as the training cases)?
(Note: I encountered this situation while creating a new mining model that was identical to an existing one. Even though the models received the exact same inputs and training cases, they yielded different results. I was able to reproduce the behavior by using steps 1-6 above, though.)
Can someone provide some insight on this behavior, or some kind of explanation of what may be happening?
I would like to find information on Clustered and Non-clustered indexes and how B-trees are used. I know a clustered index is placed into a b-tree which makes sense for fast ordered searching. What data structure does a non-clustered index use and how? I tried to find info. on the web but couldn't get much detail...
