All Questions
Tagged with model-selection lasso
51
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8
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Can you deduce if a lasso model has a smaller/larger/equal RSS to a forward selection model?
I came across this question in my exam. Where there is a table where the columns are the different model selection methods: OLS, Lasso, Forward_Size1, ForwardSize2. And the rows are the predictors, ...
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24
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What exactly is the right approach when trying to find OOS MSE when using linear lasso regression?
This isn't a question where I have a code example to provide. It is more of an informal question about what to do between 2 options.
Assume I have some data and my goal is to fit a model using the ...
- 1
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44
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What is the best model selection method for high-dimensional linear regression?
Model selection (best subset selection) in linear regression is quite important in many applications. Among the methods belonging to different frameworks such as information criterion, hypothesis ...
- 31
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1
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355
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Forcing covariates to always be part of a Lasso model
I want to use a Lasso to predict outcomes for different policy scenarios. At the optimal degree of regularization obtained by cross-validation, one important variable in whose impact I'm interested in ...
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1
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0
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307
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AIC/BIC Based Model Selection And Sample Size
I am using BIC to tune a lasso estimation and select the features that will be used in further analysis. The data is quite large, and I have some prior domain knowledge on it, so I split it by several ...
- 81
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283
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Interpretation of Elastic net having too low or high value of alpha
Often I found the situation that the elastic model what I fitted has optimal alpha value at 0 or 1. Or not only that situation, but also there some alphas go near to 0 or 1.(ex. 0.1 or 0.9)
My ...
10
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1
answer
495
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Does Lasso make irrelevant the need for coefficient significance testing?
Since Lasso selects the optimal predictors to include in the model, does this suggest that we don't need to do any of the typical significance testing that comes with OLS regression and logistic ...
- 3,273
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71
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If two models have similar predictive power, why should we prefer the one with fewer parameters?
Was thinking a bit about model selection earlier, and I ended up getting hung up on the question: “If two models have similar predictive power, which model should I select?”
For example, we often ...
- 1,577
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927
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How to compare two LASSO models - is there an equivalent to AIC/BIC?
It is often stated online that competing OLS models explaining a common dependent variable y can be compared by calculating an AIC or BIC for each fit, and that the model with the lowest value should ...
2
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1
answer
1k
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Lagged values in a Lasso regression
While working on the statistics for my thesis, I became confused while building up my model.
I am currently working on a forecasting model with the use of a LASSO regression.
The model is build as ...
- 23
1
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98
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Comparison of simple linear regression, stepwise, lasso, and ridge
I am a totally beginner of machine learning. Please understand if my question is somehow basic. :)
I have a dataset of 25 features related to a rental house and I want to predict the price based on ...
2
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0
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329
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When to use LSTM vs Lasso/Ridge Regression vs ARIMA?
I have a set of N time series and want to make predictions about the future values of these N elementary time signals.
From a first rough analysis, I can say that at a given moment in time, the N ...
- 123
8
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1
answer
5k
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AIC and its degrees of freedom for linear regression models
I have a dataset $S$ with $D$ features and three fitted linear regression models:
Model1. Ridge regression that is fitted on all $D$ features from $S$.
Model2. Ridge regression that is fitted on some $...
- 1,008
1
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0
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30
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Elastic Net: diverging number of parameters
I am reading the paper On The Adaptive Elastic-Net With a Diverging Number of Parameters by Zou and Zhang (2009). I found it while I was researching the lasso and elastic net in general and I am ...
- 11
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105
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How to pick the model that minimizes the mean absolute error when the amount of observations is small
I am given a data set with 1 target variable and 12 features for only 18 observations. My goal is to build a model that has the smallest expected prediction error.
I am allowed to use simple methods ...
