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

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45
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8answers
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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 ...
37
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3answers
12k views

Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?

In what circumstances would you want to, or not want to scale or standardize a variable prior to model fitting? And what are the advantages / disadvantages of scaling a variable?
34
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3answers
11k views

Feature selection and cross-validation

I have recently been reading a lot on this site (@Aniko, @Dikran Marsupial, @Erik) and elsewhere about the problem of overfitting occuring with cross validation - (Smialowski et al 2010 Bioinformatics,...
32
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3answers
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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 ...
31
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6answers
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Variable selection for predictive modeling really needed in 2016?

This question has been asked on CV some yrs ago, it seems worth a repost in light of 1) order of magnitude better computing technology (e.g. parallel computing, HPC etc) and 2) newer techniques, e.g. [...
28
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4answers
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When wouldn't I use LASSO for model selection?

Assume that you need to build a linear model to make predictions for new observations, and that there is uncertainty about which subset of variables should be included in the model. You are only ...
26
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5answers
7k views

Detecting significant predictors out of many independent variables

In a dataset of two non-overlapping populations (patients & healthy, total $n=60$) I would like to find (out of $300$ independent variables) significant predictors for a continuous dependent ...
26
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4answers
3k views

Why is variable selection necessary?

Common data-based variable selection procedures (for example, forward, backward, stepwise, all subsets) tend to yield models with undesirable properties, including: Coefficients biased away from ...
26
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2answers
3k views

Why does the Lasso provide Variable Selection?

I've been reading Elements of Statistical Learning, and I would like to know why the Lasso provides variable selection and ridge regression doesn't. Both methods minimize the residual sum of squares ...
24
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7answers
6k 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 ...
23
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2answers
7k views

Variable importance from SVM

How to obtain a variable (attribute) importance using SVM?
22
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4answers
2k views

How can SVM 'find' an infinite feature space where linear separation is always possible?

What is the intuition behind the fact that an SVM with a Gaussian Kernel has infinite dimensional feature space?
22
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4answers
7k views

Features for time series classification

I consider the problem of (multiclass) classification based on time series of variable length $T$, that is, to find a function $$f(X_T) = y \in [1..K]\\ \text{for } X_T = (x_1, \dots, x_T)\\ \text{...
19
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3answers
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Using principal component analysis (PCA) for feature selection

I'm new to feature selection and I was wondering how you would use PCA to perform feature selection. Does PCA compute a relative score for each input variable that you can use to filter out ...
17
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2answers
3k views

Significance testing or cross validation?

Two common approaches for selecting correlated variables are significance tests and cross validation. What problem does each try to solve and when would I prefer one over the other?
17
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2answers
2k views

Best approach for model selection Bayesian or cross-validation?

When trying to select among various models or the number of features to include for, say prediction I can think of two approaches. Split the data into training and test sets. Better still, use ...
16
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5answers
1k views

What can cause PCA to worsen results of a classifier?

I have a classifier that I'm doing cross-validation on, along with a hundred or so features that I'm doing forward selection on to find optimal combinations of features. I also compare this against ...
16
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5answers
29k views

Using LASSO from lars (or glmnet) package in R for variable selection

Sorry if this question comes across a little basic. I am looking to use LASSO variable selection for a multiple linear regression model in R. I have 15 predictors, one of which is categorical(will ...
15
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3answers
308 views

Is building a multiclass classifier better than several binary ones?

I need to classify URLs into categories. Say I have 15 categories that I'm planning to zero down every URL to. Is a 15-way classifier better? Where I have 15 labels and generate features for each ...
15
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3answers
1k views

Model stability when dealing with large $p$, small $n$ problem

Intro: I have a dataset with a classical "large p, small n problem". The number available samples n=150 while the number of possible predictors p=400. The outcome is a continuous variable. I want ...
14
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2answers
15k views

How to deal with multicollinearity when performing variable selection?

I have a dataset with 9 continuous independent variables. I'm trying to select amongst these variables to fit a model to a single percentage (dependent) variable, ...
14
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2answers
12k views

Feature selection with Random Forests

I have a dataset with mostly financial variables (120 features, 4k examples) which are mostly highly correlated and very noisy (technical indicators, for example) so I would like to select about max ...
14
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2answers
624 views

Bayesian variable selection — does it really work?

I thought I might toy with some Bayesian variable selection, following a nice blog post and the linked papers therein. I wrote a program in rjags (where I am quite a rookie) and fetched price data ...
13
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5answers
3k 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 ...
12
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3answers
23k views

How does one interpret SVM feature weights?

I am trying to interpret the variable weights given by fitting a linear SVM. (I'm using scikit-learn): ...
11
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2answers
14k views

Significance of categorical predictor in logistic regression

I am having trouble interpreting the z values for categorical variables in logistic regression. In the example below I have a categorical variable with 3 classes and according to the z value, CLASS2 ...
11
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2answers
5k views

Finding the best features in interaction models

I have list of proteins with their feature values. A sample table looks like this: ...
11
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4answers
6k views

Low classification accuracy, what to do next?

So, I'm a newbie in ML field and I try to do some classification. My goal is to predict the outcome of a sport event. I've gathered some historical data and now try to train a classifier. I got around ...
11
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2answers
2k views

How do you select variables in a regression model?

The traditional approach to variable selection is to find variables that contribute the most to predicting a new response. Recently I learned of an alternative to this. In modeling variables that ...
10
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3answers
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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 ...
10
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2answers
718 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 ...
9
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4answers
8k views

Test accuracy higher than training. How to interpret?

I've a dataset containing at most 150 examples (split into training & test), with many features (higher than 1000). I need to compare classifiers and feature selection methods which perform well ...
9
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4answers
371 views

How can top principal components retain the predictive power on a dependent variable?

Suppose I am running a regression $Y \sim X$. Why by selecting top $k$ principle components of $X$, does the model retain its predictive power on $Y$? I understand that from dimensionality-reduction/...
9
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5answers
349 views

Understanding which features were most important for logistic regression

I've built a logistic regression classifier that is very accurate on my data. Now I want to understand better why it is working so well. Specifically, I'd like to rank which features are making the ...
9
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3answers
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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 ...
9
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4answers
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Improving the SVM classification of diabetes

I am using SVM to predict diabetes. I am using the BRFSS data set for this purpose. The data set has the dimensions of $432607 \times 136$ and is skewed. The percentage of ...
9
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2answers
480 views

Domain-agnostic feature engineering that retains semantic meaning?

Feature engineering is often an important component to machine learning (it was used heavily to win the KDD Cup in 2010). However, I find that most feature engineering techniques either destroy ...
9
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5answers
5k views

Can I use PCA to do variable selection for cluster analysis?

I have to reduce the number of variables to conduct a cluster analysis. My variables are strongly correlated, so I thought to do a Factor Analysis PCA (principal component analysis). However, if I use ...
9
votes
1answer
988 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 ...
9
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1answer
2k views

If p > n, the lasso selects at most n variables

One of the motivations for the elastic net was the following limitation of LASSO: "In the p > n case, the lasso selects at most n variables before it saturates, because of the nature of the convex ...
9
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1answer
817 views

Dealing with very large time-series datasets

I have access to a very large dataset. The data is from MEG recordings of people listening to musical excerpts, from one of four genres. The data is as follows: 6 Subjects 3 Experimental repetitions ...
9
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0answers
173 views

Speed, computational expenses of PCA, LASSO, elastic net

I am trying to compare computational complexity / estimation speed of three groups of methods for linear regression as distinguished in Hastie et al. "Elements of Statistical Learning" (2nd ed.), ...
9
votes
1answer
2k 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 ...
8
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5answers
18k views

Term frequency/inverse document frequency (TF/IDF): weighting

I've got a dataset which represents 1000 documents and all the words that appear in it. So the rows represent the documents and the columns represent the words. So for example, the value in cell $(i,...
8
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5answers
22k views

Feature Selection Packages in R, which do both regression and classification

I am very new to R. I am learning machine learning right now. Very sorry, if this question appears to be very basic. I am trying to find a good feature selection package in R. I went through Boruta ...
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 $...
8
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4answers
3k views

Is there a way to use cross validation to do variable/feature selection in R?

I have a data set with about 70 variables that I'd like to cut down. What I'm looking to do is use CV to find most useful variables in the following fashion. 1) Randomly select say 20 variables. ...
8
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5answers
773 views

How to prepare/construct features for anomaly detection (network security data)

My goal is to analyse network logs (e.g., Apache, syslog, Active Directory security audit and so on) using clustering / anomaly detection for intrusion detection purposes. From the logs I have a lot ...
8
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3answers
2k views

Feature selection using mutual information in Matlab

I am trying to apply the idea of mutual information to feature selection, as described in these lecture notes (on page 5). My platform is Matlab. One problem I find when computing mutual information ...
8
votes
1answer
2k views

Feature selection and parameter tuning with caret for random forest

I have data with a few thousand features and I want to do recursive feature selection (RFE) to remove uninformative ones. I do this with caret and RFE. However, I started thinking, if I want to get ...