Timeline for Building a linear regression model for every combination vs only one Machine Learning model
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 15, 2021 at 14:24 | vote | accept | DPM | ||
| Nov 15, 2021 at 12:00 | answer | added | Erwan | timeline score: 1 | |
| Nov 15, 2021 at 0:04 | comment | added | DPM | Features are not the same as independent variables here. Categorical values, unless encoded cannot be input of a linear equation, therefore, they are not independent variables in a linear equation. And that is the case here, these categorical values cannot be encoded. I edited the question to add some context. | |
| Nov 14, 2021 at 23:51 | history | edited | DPM | CC BY-SA 4.0 |
added 701 characters in body
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| Nov 14, 2021 at 23:36 | comment | added | Erwan | I think there is some confusion about the terms: features = independent variables = usually called x = the input information for the model. target = dependent variable = usually called y = the output that the model predicts. | |
| Nov 14, 2021 at 23:22 | comment | added | Peter | What is $y$ and $x$ here? I suppose you want to include the categorical features as $x$ (independent variables). Did you check „dummy encoding“. The question/problem is not clear to me. | |
| S Nov 14, 2021 at 23:05 | review | First questions | |||
| Nov 15, 2021 at 4:38 | |||||
| S Nov 14, 2021 at 23:05 | history | asked | DPM | CC BY-SA 4.0 |