Methods and principles of selecting a subset of attributes for use in further modelling
0
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0answers
192 views
What are the most relevant metrics for social games, and how are they calculated?
For example some common ones are engagement, churn, ARPU. The problem with the metrics I mentioned (besides needen to know more) is that I do not understand clearly how are they measure and what they ...
8
votes
3answers
2k views
How to apply LASSO to IRLS (logistic regression)?
I have programmed a logistic regression using the IRLS algorithm. I would like to apply a LASSO penalization in order to automatically select the right features. At each iteration, the following is ...
2
votes
1answer
61 views
Analysis of variables of varying numbers
I work with amino acid sequences and I want to use a self-made model to tell me something about it, lets call it $f(\text{seq})$. Now i want to know the contribution of every position in the sequence ...
8
votes
2answers
1k views
Soft-thresholding vs. Lasso penalization
I am trying to summarize what I understood so far in penalized multivariate analysis with high-dimensional data sets, and I still struggle through getting a proper definition of soft-thresholding vs. ...
1
vote
2answers
163 views
How best to reduce dimensionality of a dataset composed of events and trials?
I'm trying to reduce dataset dimensionality. PCA is a good metric but that gives me new dataset. My goal is to determine from number of events (e.g. 60) and number of trials (e.g. 6) which events are ...
23
votes
6answers
2k views
Feature selection for “final” model when performing cross-validation in machine learning
I am getting a bit confused about feature selection and machine learning
and I was wondering if you could help me out. I have a microarray dataset that is
classified into two groups and has 1000s of ...
15
votes
2answers
1k views
7
votes
2answers
758 views
Computing best subset of predictors for linear regression
For the selection of predictors in multivariate linear regression with $p$ suitable predictors, what methods are available to find an 'optimal' subset of the predictors without explicitly testing all ...
9
votes
4answers
757 views
Application of machine learning techniques in small sample clinical studies
What do you think about applying machine learning techniques, like Random Forests or penalized regression (with L1 or L2 penalty, or a combination thereof) in small sample clinical studies when the ...
-1
votes
1answer
234 views
Shall I trust AIC (non-full model) or slope (full model)?
The purpose to run regressions for butterfly richness again 5 environmental variables is to show the importance rank of the independent variables mainly by AIC.
In non-full models, they reveal that ...
12
votes
6answers
1k views
Variable selection procedure for binary classification
What are the variable/feature selection that you prefer for binary classification when there are many more variables/feature than observations in the learning set? The aim here is to discuss what is ...
6
votes
6answers
1k views
Algorithms and methods for attribute/feature selection?
I have data with continuous class and I'm searching for good methods to reduce number of attributes. Now I'm using correlation based filters, random forests and Gram–Schmidt algorithm.
What I want ...
