Methods and principles of selecting a subset of attributes for use in further modelling

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2
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2answers
481 views

Variables importance: who can do the most pushups?

I don't know enough math to formulate an intelligent question on this so I'll give an example. I'd like an answer to my example but also I'd like to know the jargon I need to be able to research it ...
5
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2answers
473 views

Feature selection for low probability event prediction

I'm currently trying to predict the probability for low probability events (~1%). I have large DB with ~200,000 vectors (~2000 plus examples) with ~200 features. I'm trying to find the the best ...
6
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1answer
1k views

Clustering probability distributions - methods & metrics?

I have some data points, each containing 5 vectors of agglomerated discrete results, each vector's results generated by a different distribution, (the specific kind of which I am not sure, my best ...
3
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2answers
389 views

Feature selection and latent variables

I would like to know if it is useful (or maybe dangerous) to reduce the number of attributes (by selecting the most informative ones among thousands) before seeking for latent variables or not (in an ...
2
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0answers
580 views

Error using rfe in caret package in R

I am doing some exploratory data analysis in the Heritage Health Prize , and have come across a weird error using R's caret package. In the dataset, I've created a dataframe counting how many times a ...
3
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4answers
1k views

Proper variable selection method for glm

I have a mixed model with a continuous outcome variable and a certain number of predictors. Some need to be included in the model no matter what (sex, age, and a "main factor"), and others must be ...
1
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0answers
154 views

Measures of predictive power of attributes in data mining

What are the most widely used measures of predictive power of attributes in scoring models? Motivation: I have a lot of attributes, more than I can study by myself and I want to select somehow the ...
1
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1answer
222 views

Variable selection for increasing accuracy

I know that there are various posts regarding variable selection but I am asking something particular. With respect to the question that I posted today in the following link: Low accuracy in out of ...
2
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0answers
118 views

New development in variable selection in clustering using MCMC?

The latest general framework I know in MCMC-based wrapper method(doing variable selection and clustering simultaneously) are the paper "Bayesian variable selection in clustering high-dimensional data" ...
9
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1answer
681 views

Random permutation test for feature selection

I am confused about permutation analysis for feature selection in a logistic regression context. Could you provide a clear explanation of the random permutation test and how does it applies to feature ...
3
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4answers
290 views

Suggestions for identifying key features

I have a large set of customer data. For these customers, I have devised a customer loyalty score which is a measure of the loyalty of the customer. I want to find the features that are strongly ...
3
votes
1answer
540 views

How can I assess how descriptive feature vectors are?

I am assessing how good different features are for unsupervised classification of a set of objects. For each different feature I test, I have computed a feature vector that describes the object. I ...
0
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1answer
191 views

How to get scored combination of features

My data looks like this (F=Features) ...
2
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1answer
200 views

Interpretation of “one” feature change in a supervised classifier

i'm making experiments using app. 5000 labeled dataset.i'm trying different supervised ML algorithm to evaluate the results.The vector size is 13 with the labels (totally 12 features+1 label) and i ...
9
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3answers
1k views

The use of median polish for feature selection

In a paper I was reading recently I came across the following bit in their data analysis section: The data table was then split into tissues and cell lines, and the two subtables were separately ...
5
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2answers
1k views

Is it possible to use kernel PCA for feature selection?

Is it possible to use kernel principal component analysis (kPCA) for Latent Semantic Indexing (LSI) in the same way as PCA is used? I perform LSI in R using the prcomp PCA function and extract the ...
21
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3answers
5k views

What are disadvantages of using the lasso for variable selection for regression?

From what I know, using lasso for variable selection handles the problem of correlated inputs. Also, since it is equivalent to Least Angle Regression, it is not slow computationally. However, many ...
9
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2answers
594 views

Best methods of feature selection for nonparametric regression

A newbie question here. I am currently performing a nonparametric regression using the np package in R. I have 7 features and using a brute force approach I identified the best 3. But, soon I will ...
4
votes
4answers
148 views

Feature selection for classification, controlling for sub-population

I have a bunch of points that belong to one of population P1, P2, ... Pn AND to class A or B. Within each population I'll be doing classification between A and B, and I want to select features that ...
8
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1answer
194 views

How to quantify redundancy of features?

I have three features that I use to solve a classification problem. Originally, these features produced boolean values, so I could evaluate their redundancy by looking at how much the sets of positive ...
2
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0answers
160 views

What should I do to compare different sets of data?

I am a beginner in statistics, and I want to learn machine learning :). Therefore, I have gathered some sample data to practice. But, the problem is I want to create a feature (or attribute), which is ...
0
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0answers
218 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 ...
9
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3answers
3k 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
66 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
2k 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. ...
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2answers
213 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 ...
31
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8answers
5k 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 ...
18
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2answers
3k views

Variable importance from SVM

How to obtain a variable (attribute) importance using SVM?
8
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3answers
1k 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 ...
10
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4answers
1k 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
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1answer
250 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 ...
20
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6answers
3k 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 ...
7
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6answers
2k 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 ...