Timeline for Questions regarding linear regression as we change the parameters (related to Andrew Ng's course)
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 16, 2017 at 15:09 | comment | added | Digio | In statistical modelling you either assume, know, or find the structure of a model through trial and error. In machine learning, you can use a universal function approximator such a neural network to do the job for you. I would advise you to start a new question for a more detailed answer. | |
| Sep 14, 2017 at 15:48 | comment | added | bru1987 | Ok so let's say that we want to do a polynomial regression (so it contains multiple features and the input parameters are squared, cubed etc). There are a number of input variables ($x_1$ for the area of the house, $x_2$ for the average price of the houses next to it and so on). How does one choose which variables ($x_1$, $x_2$...) should be squared, which should be cubed and so on, so that we build the hypothesys as (for example) $$h_\theta = \theta_0+\theta_1 x_1^3 + \theta_2 x_2 + \theta_3 x_3^2 + \cdots$$ and our model calculates the appropriate values of the parameters? | |
| Sep 14, 2017 at 14:33 | vote | accept | bru1987 | ||
| Sep 14, 2017 at 12:54 | comment | added | Digio | Multivariate regression refers to a model with multiple response variables. A model with multiple independent variables as described in (3) falls under multiple regression. | |
| Sep 14, 2017 at 11:21 | comment | added | bru1987 | Hi @Digio, first of all thank your input. Can I ask you a couple of things on top of your answer, if you would be so kind to clarify? You say that (2.) and (3.) are still linear models, but what is this "multivariate linear regression" I see around? How's this related to (1.), (2.) and (3.)? | |
| Sep 13, 2017 at 13:17 | history | edited | Digio | CC BY-SA 3.0 |
added 417 characters in body
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| Sep 13, 2017 at 13:09 | history | answered | Digio | CC BY-SA 3.0 |