Timeline for What is meant by multicollinearity does not influence the predictive power of the model?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| May 2, 2020 at 12:45 | comment | added | Sextus Empiricus | A less abstract example $4 x_1 - 2 x_2$ may be very close to $-2 x_1 + 4 x_2$ but is completely opposite to $-4 x_1 + 2 x_2$ | |
| May 2, 2020 at 12:34 | comment | added | Sextus Empiricus | @PeterFlom the signs change in the sense that negative values become positive values and vice versa, but you are not getting the strong sense of sign changes, where the coefficients are 'flipped' and multiplied with '-1'. | |
| May 2, 2020 at 12:10 | comment | added | Peter Flom | @SextusEmpiricus I'm not sure how what you wrote differs from what I wrote. | |
| May 2, 2020 at 12:08 | comment | added | Peter Flom | @divyamsureka Not necessarily, but it might. | |
| May 1, 2020 at 20:58 | comment | added | divyam sureka | @PeterFlom, you said that any tiny changes in the existing data will produce completely different results, so suppose i am estimating price of a house with dependent variables size, high school in the neighborhood, So in my training data if my size changes from 1000 to 1001, it would produce completely different results? | |
| May 1, 2020 at 16:27 | comment | added | Sextus Empiricus | The final part about flipping the signs is not so intuitive to me. This only makes sense when the value is close to zero. Maybe you meant $$b_1 x_1 + b_2 x_2 \approx b_2 x_1 + b_1 x_2$$ and when $b_1$ and $b_2$ have a different sign then switching them means that the signs of the coefficients have changed while the value is still much the same. | |
| May 1, 2020 at 13:09 | history | answered | Peter Flom | CC BY-SA 4.0 |