479

-1

-1

Published on

Data Applied: Decision

No Downloads

Total Views

479

On Slideshare

0

From Embeds

0

Number of Embeds

0

Shares

0

Downloads

0

Comments

0

Likes

1

No embeds

No notes for slide

- 1. Data-Applied.com: Decision<br />
- 2. Introduction<br />Decision trees let you construct decision models<br />They can be used for forecasting, classification or decision<br />At each branch the data is spit based on a particular field of data<br />Decision trees are constructed using Divide and Conquer techniques <br />
- 3. Divide-and-Conquer: Constructing Decision Trees<br />Steps to construct a decision tree recursively:<br />Select an attribute to placed at root node and make one branch for each possible value <br />Repeat the process recursively at each branch, using only those instances that reach the branch<br /> If at any time all instances at a node have the classification, stop developing that part of the tree<br />Problem: How to decide which attribute to split on<br />
- 4. Divide-and-Conquer: Constructing Decision Trees<br />Steps to find the attribute to split on:<br />We consider all the possible attributes as option and branch them according to different possible values<br />Now for each possible attribute value we calculate Information and then find the Information gain for each attribute option<br />Select that attribute for division which gives a Maximum Information Gain<br />Do this until each branch terminates at an attribute which gives Information = 0 <br />
- 5. Divide-and-Conquer: Constructing Decision Trees<br />Calculation of Information and Gain:<br />For data: (P1, P2, P3……Pn) such that P1 + P2 + P3 +……. +Pn = 1 <br />Information(P1, P2 …..Pn) = -P1logP1 -P2logP2 – P3logP3 ……… -PnlogPn<br />Gain = Information before division – Information after division <br />
- 6. Divide-and-Conquer: Constructing Decision Trees<br />Example:<br />Here we have consider each<br />attribute individually<br />Each is divided into branches <br />according to different possible <br />values <br />Below each branch the number of<br />class is marked <br />
- 7. Divide-and-Conquer: Constructing Decision Trees<br />Calculations:<br />Using the formulae for Information, initially we have<br />Number of instances with class = Yes is 9<br /> Number of instances with class = No is 5<br />So we have P1 = 9/14 and P2 = 5/14<br />Info[9/14, 5/14] = -9/14log(9/14) -5/14log(5/14) = 0.940 bits<br />Now for example lets consider Outlook attribute, we observe the following:<br />
- 8. Divide-and-Conquer: Constructing Decision Trees<br />Example Contd.<br />Gain by using Outlook for division = info([9,5]) – info([2,3],[4,0],[3,2])<br /> = 0.940 – 0.693 = 0.247 bits<br />Gain (outlook) = 0.247 bits<br /> Gain (temperature) = 0.029 bits<br /> Gain (humidity) = 0.152 bits<br /> Gain (windy) = 0.048 bits<br />So since Outlook gives maximum gain, we will use it for division<br />And we repeat the steps for Outlook = Sunny and Rainy and stop for Overcast since we have Information = 0 for it <br />
- 9. Divide-and-Conquer: Constructing Decision Trees<br />Highly branching attributes: The problem<br />If we follow the previously subscribed method, it will always favor an attribute with the largest number of branches<br />In extreme cases it will favor an attribute which has different value for each instance: Identification code<br />
- 10. Divide-and-Conquer: Constructing Decision Trees<br />Highly branching attributes: The problem<br />Information for such an attribute is 0<br />info([0,1]) + info([0,1]) + info([0,1]) + …………. + info([0,1]) = 0<br />It will hence have the maximum gain and will be chosen for branching<br />But such an attribute is not good for predicting class of an unknown instance nor does it tells anything about the structure of division<br />So we use gain ratio to compensate for this <br />
- 11. Divide-and-Conquer: Constructing Decision Trees<br />Highly branching attributes: Gain ratio<br />Gain ratio = gain/split info<br />To calculate split info, for each instance value we just consider the number of instances covered by each attribute value, irrespective of the class<br />Then we calculate the split info, so for identification code with 14 different values we have:<br />info([1,1,1,…..,1]) = -1/14 x log1/14 x 14 = 3.807<br />For Outlook we will have the split info:<br />info([5,4,5]) = -1/5 x log 1/5 -1/4 x log1/4 -1/5 x log 1/5 = 1.577<br />
- 12. Decision using Data Applied’s web interface<br />
- 13. Step1: Selection of data<br />
- 14. Step2: SelectingDecision<br />
- 15. Step3: Result<br />
- 16. Visit more self help tutorials<br /><ul><li>Pick a tutorial of your choice and browse through it at your own pace.
- 17. The tutorials section is free, self-guiding and will not involve any additional support.
- 18. Visit us at www.dataminingtools.net</li>

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment