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Is lasso preferable to ridge or principal component regression in multicollinear settings?

I made some computational investigations into this issue and I concluded that perhaps it is actually a problem. Here is my Python code: ...
Tomo's user avatar
  • 141
-3 votes

Does it make sense to deal with multicollinearity prior to LASSO regression?

Do the following and you should be fine: Run a LASSO regression to select variables Check correlation/VIF on the selected variables (correlated variables lead to misleading estimates of your ...
Esben Eickhardt's user avatar
9 votes
Accepted

Motivation for automated variable selection in case of p>n

What you wrote is false. For instance, no matter how high an order you go for with polynomials, you cannot ever perfectly fit the data when you have multiple distinct $y$ values with the same feature ...
Dave's user avatar
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2 votes

Does penalizing the slope in Ridge/ Lasso regression has adverse effect based on the training data?

If your train and testset are not similar, nothing makes sense. The whole idea of statistical learning, machine learning, it is all based on the idea the the test- and trainset are more or less ...
Gijs's user avatar
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3 votes
Accepted

Does penalizing the slope in Ridge/ Lasso regression has adverse effect based on the training data?

First, if these are the training and test data (or if they look anything like this) then your system of randomizing observations to "train" and "test" is seriously messed up, ...
Peter Flom's user avatar
  • 124k
2 votes
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Implementations of Lasso in Python and R?

LASSO in Python With Python, in my experience, the most common implementation of LASSO (Least Absolute Shrinkage and Selection Operator) is provided by the ...
Robert Long's user avatar
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0 votes

Is lasso preferable to ridge or principal component regression in multicollinear settings?

Lasso vs Ridge, the difference between these two is whether you want to place more or less focus on parameter selection or on regularisation. Ridge will prefer more parameters but with more shrinking, ...
Sextus Empiricus's user avatar
2 votes

Finding the corners of noisy polygons

The following (using Mathematica) does not do what one would do "by eye" but that's because the data points don't fall perfectly on a desired number of line segments. This uses Mathematica's ...
JimB's user avatar
  • 3,930
1 vote
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Finding the corners of noisy polygons

Here's a shot at it using the Douglas-Peuker algorithm as whuber suggested. Using MATLAB ...
sav's user avatar
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