{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

lecture7 - Data Mining CS57300 Purdue University Predictive...

Info iconThis preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
Data Mining CS57300 Purdue University September 16, 2010
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Predictive modeling: representation
Background image of page 2
Data mining components • Task specification: Prediction • Data representation: Homogeneous IID data • Knowledge representation • Learning technique • Inference technique
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Descriptive vs. predictive modeling • Descriptive models summarize the data • Provide insights into the domain • Focus on modeling joint distribution P(X) • May be used for classification, but not primary goal • Predictive models predict the value of one variable of interest given known values of other variables • Focus on modeling conditional distribution P(Y | X) or decision boundary for Y
Background image of page 4
Example: SPAM • I was reading a little more about Tsalling entropy and trying to figure out whether it would be appropriate for relational learning problems. One possibility is to use it for exponential random graph models, which have features like the number of triangles in the graph. Since these grow with graph size, it seems to be an "extensive" property that the Tsalling entropy is trying to model…
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}