HADOOP David Kauchak CS451 – Fall 2013. Admin Assignment 7.

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Presentation transcript:

HADOOP David Kauchak CS451 – Fall 2013

Admin Assignment 7

logistic regression: three views linear classifier conditional model logistic linear model minimizing logistic loss

Logistic regression Why is it called logistic regression?

A digression: regression vs. classification Raw dataLabel extract features f 1, f 2, f 3, …, f n featuresLabel classification: discrete (some finite set of labels) regression: real value

linear regression Given some points, find the line that best fits/explains the data Our model is a line, i.e. we’re assuming a linear relationship between the feature and the label value How can we find this line? f1f1 response (y)

Linear regression Learn a line h that minimizes some loss/error function: feature (x) response (y) Sum of the individual errors: 0/1 loss!

Error minimization How do we find the minimum of an equation? Take the derivative, set to 0 and solve (going to be a min or a max) Any problems here? Ideas?

Linear regression feature response squared error is convex!

Linear regression Learn a line h that minimizes an error function: in the case of a 2d line: function for a line feature response

Linear regression We’d like to minimize the error Find w 1 and w 0 such that the error is minimized We can solve this in closed form

Multiple linear regression If we have m features, then we have a line in m dimensions weights

Multiple linear regression We can still calculate the squared error like before Still can solve this exactly!

Logistic function

Logistic regression Find the best fit of the data based on a logistic

Big Data What is “big data”? What are some sources of big data? What are the challenges of dealing with big data? What are some of the tools you’ve heard of? For more info:

Hadoop: guest speaker Patricia Florissi CTO of EMC PhD from Columbia University

Hadoop