CS 408 – Machine Learning

LECTURE 6REGRESSION AND CORRELATION

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

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CS 408 – Machine Learning

Solution: Find Multiple Regression Equation by Excel

Help : http://www.youtube.com/watch?v=FEFXp0iilsU

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CS 408 – Machine Learning

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CS 408 – Machine Learning

LOGISTIC REGRESSION

Logistic Regression calculates the probability of the event occurring, such as the purchase of a product.

In general, the thing being predicted in a Regression equation is represented by the dependent variable or output variable and is usually labeled as the Y variable in the Regression equation.

In the case of Logistic Regression, this “Y” is binary. In other words, the output or dependent variable can only take the values of 1 or 0. The predicted event either occurs or it doesn’t occur – your prospect either will buy or won’t buy.

Occasionally this type of output variable also referred to as a Dummy Dependent Variable.

What is the difference between linear regression and logistic regression?

Linear Regression:  is used to establish a relationship between Dependent and Independent variables, which is useful in estimating the resultant dependent variable in case independent variable changes.

Example - Using a Linear Regression, the relationship between Rain (R) and Umbrella Sales (U) is found to be - U = 2R + 5000This equation says that for every 1mm of Rain, there is a demand for 5002 umbrellas. So, using Simple Regression, you can estimate the value of your variable.

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CS 408 – Machine Learning

Logistic Regression:  on the other hand is used to ascertain the probability of an event. And this event is captured in binary format, i.e. 0 or 1.

Example - I want to ascertain if a customer will buy my product or not. For this, I would run a Logistic Regression on the (relevant) data and my dependent variable would be a binary variable (1=Yes; 0=No).

In terms of graphical representation, Linear Regression gives a linear line as an output, once the values are plotted on the graph. Whereas, the logistic regression gives an S-shaped line.

Question:

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CS 408 – Machine Learning

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