METHOD AND APPARATUS FOR VISUALIZING AND INTERACTING WITH DECISION TREES

• US 20130117280A1
• Filed: 11/02/2012
• Published: 05/09/2013
• Est. Priority Date: 11/04/2011
• Status: Abandoned Application

• Current Assignee: BigML, Inc.
• Original Assignee: BigML, Inc.
• Inventors: Ashenfelter, Adam, Martin, Francisco, Parker, Charles, Verwoerd, Jos, DONALDSON, J. Justin, Araujo, Miguel, Ortega, Jose Antonio
• Application Number:
  US13/667,542
• Publication Number:
  US 20130117280A1
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/17 Details of further file system functions
• CPC Class Codes: G06F 16/9027 Trees
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957

Abstract

A decision tree model is generated from sample data. A visualization system may automatically prune the decision tree model based on characteristics of nodes or branches in the decision tree or based on artifacts associated with model generation. For example, only nodes or questions in the decision tree receiving a largest amount of the sample data may be displayed in the decision tree. The nodes also may be displayed in a manner to more readily identify associated fields or metrics. For example, the nodes may be displayed in different colors and the colors may be associated with different node questions or answers.

Claims

1. A method comprising:

generating a decision tree from sample data;

identifying characteristics associated with the decision tree; and

filtering out portions of the decision tree model based on the characteristics.

2. The method of claim 1, wherein the decision tree comprises nodes and branches and filtering the decision tree model comprises filtering out some of the nodes and branches based on the characteristics of the decision tree associated with the nodes or branches.

3. The method of claim 1, further comprising:

identifying a subset of nodes in the decision tree receiving largest amounts of the sample data; and

displaying only the subset of nodes in the decision tree.

4. The method of claim 1, further comprising:

identifying a subset of questions in the decision tree receiving largest amounts of the sample data; and

displaying only nodes in the decision tree associated with the subset of questions.

5. The method of claim 1, further comprising:

identifying at least one of questions, outputs, and/or metrics associated with nodes in the decision tree; and

displaying identifiers in the decision tree associated with the questions, outputs, and/or metrics.

6. The method of claim 5, wherein the identifiers comprise colors and displaying the identifiers comprises displaying the nodes with the colors.

7. The method of claim 5, wherein the identifiers comprise text in a popup window.

8. The method of claim 7, further comprising displaying the popup windows in response to receiving an input selecting or hovering over the nodes.

9. The method of claim 5, wherein the identifiers comprise a legend containing text displaying the questions, outputs, and/or metrics.

10. The method of claim 5, wherein the identifiers comprise alphanumeric charters and displaying the identifiers comprises displaying the alphanumeric characters in the nodes.

11. The method of claim 5, wherein one of the metrics comprises amounts of the sample data received by the nodes.

12. The method of claim 1, further comprising:

identifying amounts of sample data received by nodes in the decision tree; and

displaying different thicknesses of branches attached to the nodes based on the amounts of sample data received by nodes.

13. The method of claim 1, further comprising:

receiving an input identifying a selected node in the decision tree;

identifying nodes within a path of the decision tree from a root node to the selected node; and

displaying questions associated with the nodes within the path of the decision tree.

14. The method of claim 1, comprising:

filtering a first set of nodes from the decision tree;

displaying a second set of remaining nodes with the decision tree;

receiving an input identifying a selected one of the second set of remaining nodes; and

displaying child nodes for the selected one of the second set of remaining nodes, wherein the child nodes are from the first set of nodes.

15. The method of claim 1, comprising:

displaying the decision tree with a first number of nodes;

receiving an input selecting a second number of nodes; and

redisplaying the decision tree with the second number of nodes.

16. The method of claim 15, further comprising:

displaying the decision tree with the first number of nodes, wherein the first number of nodes are associated with questions receiving a largest amount of the sample data; and

redisplaying the decision tree with the second number of nodes, wherein the second number of nodes are associated with questions receiving a largest amount of the sample data.

17. An apparatus, comprising:

a memory configured to store sample data; and

a processing device configured to;

generate a model from the sample data;

identify metrics for the model; and

display a decision tree for the model based on the metrics.

18. The apparatus of claim 17, wherein the processing device is configured to identify fields associated with nodes in the decision tree and display the nodes in different colors associated with the fields.

19. The apparatus of claim 17, wherein the processing device is configured to identify outputs associated with nodes in the decision tree and display the nodes in different colors corresponding to the associated outputs.

20. The apparatus of claim 17, wherein the metrics identify instances of the sample data received by nodes in the decision tree and the processor is configured to only display a predetermined number of the nodes receiving a largest number of the instances of the sample data.

21. The apparatus of claim 17, wherein the metrics comprise a number of instances of the sample data received by nodes in the decision tree and the processing device is configured to display branches in the decision tree with thicknesses associated with the number instances.

22. The apparatus of claim 17, wherein the processing device is further configured to:

display nodes in the decision tree in different colors; and

display a legend mapping the colors to questions associated with the nodes.

23. The apparatus of claim 17, wherein the processing device is further configured to:

detect an input selecting a node in the decision tree; and

display a percentage of instances of the sample data used by the node.

24. The apparatus of claim 17, wherein the processing device is further configured to:

detect an input selecting a node in the decision tree; and

display one of more of the following in response to the input;

a question associated with the node;

an output associated with the node; and

/or a number of instances of the sample data used by the node.

25. The apparatus of claim 17, wherein the processing device is further configured to:

display a first number of nodes in the decision tree;

receive an input selecting a second number of nodes; and

redisplay the decision tree with the second number of nodes.

26. The apparatus of claim 17, wherein the processing device is further configured to:

generate a ranking of nodes in the decision tree based on importance; and

display a subset of the nodes in the decision tree based on the ranking.

27. The apparatus of claim 26, wherein the processing device is configured to generate the ranking of the nodes based on confidence values for the nodes predicting correct answers.