AI:Regression Problems
Revision as of 22:07, 6 April 2019 by Hdridder (talk | contribs) (Created page with "Category:AI Learning from a training set. A training set has m samples of x's (input variables or features) and the resulting y's (output/target variables) The learning...")
Learning from a training set.
A training set has m samples of x's (input variables or features) and the resulting y's (output/target variables)
The learning algorithm finds the best matching hypothesis that brings the input to the output values.
The hypothesis can be:
Linear regression with 1 variable (Univariate linear regression)
- h(x) = θ(0) + θ(1)*x
- θ are the hypothesis parameters, it is the weight a feature gets. For the multiplication table θ is just the table you are working on. So for the table of 4, in the above formula θ(1) = 4
The aim of the learning algorithm is to choose θ(0) and θ(1) so that the result for all input values is as close as possible to the given output values.
- J(θ(0),θ(1)) = 0.5m * ∑mi( h( x(i) ) = y(i) )²
\sum_{i=1}^m k^2