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  • Implement a decision tree in SharePoint

    - by ria
    What is the best way to implement a decision tree in SharePoint? Is there a web part available? Does any of Sharepoint's Fab 40 templates contain a decision tree web part? i have searched but i couldnt find a useful answer anywhere. Please suggest.

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  • Realizing program with decision tree logics

    - by Vytas999
    The system realizes a game “Think animal”. Main use case is: 1. System offers user to think about any animal and the system will try to guess it 2. The system starts asking questions from the start of decision tree. Ex., “Question: It has fur?”, and provides possible answers – yes or no. 3. If the user answers Yes, the system proceeds to these steps: a. System tries to guess animal that has that feature, ex. “My guess: Is it bear?” and provides with possible answers – yes or no. b. If the user answer is Yes, the system offers to think off another animal 4. If the user answers is No, the system moves to No node in decision tree and moves to 2 step (and starts from asking from new node). 5. If system runs out of nodes (i.e., empty yes or no node was reached): a. the system announces that it has given up, and ask user to enter: i. What animal he had in mind ii. What is his characteristic feature b. User enters requested data c. The system creates a new node and links it to yes or no of last active node. Where i can get some information and some examples, when implementing decision tree logics in MS SQL Server and C#..? Any information will be usefull. Thanks

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  • TicTacToe AI Making Incorrect Decisions

    - by Chris Douglass
    A little background: as a way to learn multinode trees in C++, I decided to generate all possible TicTacToe boards and store them in a tree such that the branch beginning at a node are all boards that can follow from that node, and the children of a node are boards that follow in one move. After that, I thought it would be fun to write an AI to play TicTacToe using that tree as a decision tree. TTT is a solvable problem where a perfect player will never lose, so it seemed an easy AI to code for my first time trying an AI. Now when I first implemented the AI, I went back and added two fields to each node upon generation: the # of times X will win & the # of times O will win in all children below that node. I figured the best solution was to simply have my AI on each move choose and go down the subtree where it wins the most times. Then I discovered that while it plays perfect most of the time, I found ways where I could beat it. It wasn't a problem with my code, simply a problem with the way I had the AI choose it's path. Then I decided to have it choose the tree with either the maximum wins for the computer or the maximum losses for the human, whichever was more. This made it perform BETTER, but still not perfect. I could still beat it. So I have two ideas and I'm hoping for input on which is better: 1) Instead of maximizing the wins or losses, instead I could assign values of 1 for a win, 0 for a draw, and -1 for a loss. Then choosing the tree with the highest value will be the best move because that next node can't be a move that results in a loss. It's an easy change in the board generation, but it retains the same search space and memory usage. Or... 2) During board generation, if there is a board such that either X or O will win in their next move, only the child that prevents that win will be generated. No other child nodes will be considered, and then generation will proceed as normal after that. It shrinks the size of the tree, but then I have to implement an algorithm to determine if there is a one move win and I think that can only be done in linear time (making board generation a lot slower I think?) Which is better, or is there an even better solution?

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  • Visualize Classifier Error Weka

    - by user1780592
    Hye there i have a have datasets where this data i have test it on weka with J48 classifier It give me an output = 87.2611% Total of instances = 157 Correctly Instances = 137 Incorrectly instance = 20 Then i have do a visualize classifier error on my data. However my result have been decrease to: New result = 85.4015% Correctly Instances = 117 Incorrectly instances = 20 Total of instances = 137 Is there any reason for that? Should my result become much better after i do the visualize classifier error?

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  • Languages for implementing decision trees

    - by Shailesh Tainwala
    What would be a good choice of programming language in which to implement a decision tree? The results of the implementation will be for personal use only, so no need to consider ability to publish etc. I have heard that Octave is a good option, can anyone explain why a matrix based language is recommended for implementing decision trees?

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  • Parse a CSV file using python (to make a decision tree later)

    - by Margaret
    First off, full disclosure: This is going towards a uni assignment, so I don't want to receive code. :). I'm more looking for approaches; I'm very new to python, having read a book but not yet written any code. The entire task is to import the contents of a CSV file, create a decision tree from the contents of the CSV file (using the ID3 algorithm), and then parse a second CSV file to run against the tree. There's a big (understandable) preference to have it capable of dealing with different CSV files (I asked if we were allowed to hard code the column names, mostly to eliminate it as a possibility, and the answer was no). The CSV files are in a fairly standard format; the header row is marked with a # then the column names are displayed, and every row after that is a simple series of values. Example: # Column1, Column2, Column3, Column4 Value01, Value02, Value03, Value04 Value11, Value12, Value13, Value14 At the moment, I'm trying to work out the first part: parsing the CSV. To make the decisions for the decision tree, a dictionary structure seems like it's going to be the most logical; so I was thinking of doing something along these lines: Read in each line, character by character If the character is not a comma or a space Append character to temporary string If the character is a comma Append the temporary string to a list Empty string Once a line has been read Create a dictionary using the header row as the key (somehow!) Append that dictionary to a list However, if I do things that way, I'm not sure how to make a mapping between the keys and the values. I'm also wondering whether there is some way to perform an action on every dictionary in a list, since I'll need to be doing things to the effect of "Everyone return their values for columns Column1 and Column4, so I can count up who has what!" - I assume that there is some mechanism, but I don't think I know how to do it. Is a dictionary the best way to do it? Would I be better off doing things using some other data structure? If so, what?

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  • machine learning and code generator from strings

    - by BCS
    The problem: Given a set of hand categorized strings (or a set of ordered vectors of strings) generate a decision function to categorize more input. The question: are there any tools out there that will do that? I'm thinking of some kind of reasonably polished, download, install and go kind of things, as opposed to to some library or a brittle academic program.

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  • Build a decision tree for classification of large amount data,using python?

    - by kaushik
    Hi,i am working for speech synthesis.In this i have a large number of pronunciation for each phone i.e alphabet and need to classify them according to few feature such as segment size(int) and alphabet itself(string) into a smaller set suitable for that particular context. For this purpose,i have decided to use decision tree for classification.the data to be parsed is in the S expression format.eg:((question)(LEFTNODE)(RIGHTNODE)). i hav idea for building decision tree for normal buit in type such as list..looking for suggestion for implementation for S expression.. kindly help.. Thanks in advance.. Note:this question may look similar to my prev post,srry if cant giv multiple post.already edited it many times so though of wirting new question instead of editing again

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  • traverse a binary decison tree using python?

    - by kaushik
    how to traverse a binary decision tree using python language. given a tree,i want know how can we travesre from root to required leaf the feature of the required leaf are given in an dictionary form assume and have to traverse from root to leaf answering the questions at each node with the details given in feature list.. the decision tree node has format ((question)(left tree)(right tree)) while traversing it should answer question at each node and an choose left or right and traverse till leaf?

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