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

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11
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3answers
10k views

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 ...
27
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3answers
7k 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 ...
30
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3answers
8k 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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8answers
6k 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 ...
8
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2answers
7k 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 ...
17
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2answers
1k 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 ...
22
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3answers
2k 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 ...
15
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5answers
746 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 ...
14
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3answers
564 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?
2
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2answers
683 views

How to select a subset of variables from my original long list in order to perform logistic regression analysis?

My situation: small sample size: 116 binary outcome variable long list of explanatory variables: 44 explanatory variables did not come from the top of my head; their choice was based on the ...
14
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2answers
11k 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, ...
4
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3answers
13k 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 ...
1
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3answers
3k views

Use of PCA analysis to select variables for a regression analysis [duplicate]

I have too many environmental variables to use in a multiple regression analysis. If I use all the variables the models are just too complex. The use of the PCA axes in the regression analysis was ...
23
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5answers
4k 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 ...
8
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3answers
13k 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): ...
4
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2answers
2k views

Variablity in cv.glmnet results

I am using cv.glment to find predictors. The set-up I use is as follows: ...
5
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1answer
5k views

The main effect will be non-significant if the interaction is significant? [duplicate]

I am using linear mixed models to identify important factors, and it turns out that: A: significant B: not significant ...
20
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6answers
4k 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 ...
15
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2answers
1k 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 ...
8
votes
2answers
7k 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 ...
16
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2answers
2k 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?
5
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3answers
2k views

Logistic regression performance with high number of predictors

I'm trying to understand the behavior of logistic regression in high dimensional problems (i.e. when you are fitting a logistic regression to data with a high number of predictor variables). Every ...
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 ...
2
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1answer
364 views

How to perform step() when n < p in R?

I am trying to perform stepwise regression for variable selection in R. In matlab, the stepwisefit function is able to work in ...
0
votes
3answers
465 views

Feature selection before SVM

I have a simple but difficult question. Does feature selection before SVM help? I have a data set that has ~1100 features but a lot of these are redundant data / uncorrelated data. Can someone give me ...
1
vote
2answers
199 views

How to account for participants in a study design?

I have a conceptual problem. I want to find out if stress during the day leads to (stronger) teeth grinding (bruxism) at night. I have a number of participants. They will fill in a self-report ...
8
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4answers
207 views

How can top $k$ 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 ...
6
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1answer
1k views

When does LASSO select correlated predictors?

I'm using the package 'lars' in R with the following code: ...
6
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5answers
2k views

Is using the same data for feature selection and cross-validation biased or not?

We have a small dataset (about 250 samples * 100 features) on which we want to build a binary classifier after selecting the best feature subset. Lets say that we partition the data into: Training, ...
6
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3answers
2k views

How to avoid overfitting when using crossvalidation within Genetic Algorithms

This is a long set-up, but the pure intellectual challenge will make it worthwhile I promise ;-) I have marketing data where there is a treatment and a control (i.e a customer gets no treatment). The ...
5
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3answers
228 views

Regression in $p\gg N$ setting (predicting drug efficiency from gene expression with 30k predictors and ~30 samples)

I have a dataset of 29 cell lines and the IC50 values of a test drug. I want to find a relation between the gene expression profiles of each cell line (nearly 31000 genes) and the IC50 values. My ...
5
votes
3answers
596 views

Can I perform an exhaustive search with cross-validation for feature selection?

I have been reading some of the posts about feature selection and cross-validation but I still have questions about the correct procedure. Suppose I have a dataset with 10 features and I want to ...
4
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1answer
324 views

Variable selection for regression - the subselect package

No regular here will be unaware of the perils of using stepwise and similar automatic methods for variable selection in regression analysis. But preferred alternatives, such as the lasso or ...
6
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1answer
2k views

Gini decrease and Gini impurity of children nodes

I'm working on the Gini feature importance measure for random forest. Therefore, I need to calculate the Gini decrease in node impurity. Here is the way I do so, which leads to a conflict with the ...
5
votes
2answers
551 views

Why does increasing the number of features reduce performance?

I'm trying to gain an intuition as to why increasing the number of features could reduce performance. I'm currently using an LDA classifier which performs better bivariately among certain features ...
1
vote
1answer
176 views

GBM: Predict the response variable measured in {0,20}

I need to predict the response that has values in {0,20}. Should it be used as a factor or as a numeric value? How does it influence on the prediction error? I am using GBM with the Gaussian ...
0
votes
1answer
221 views

What does the varImp function in the caret package actually compute for a glmnet (elastic net) object

I am fitting an elastic net model with glmnet via the caret package with 189 predictors and a binomial criteria (a,b) ...
15
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4answers
4k 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)\\ ...
24
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3answers
6k 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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5answers
14k 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 ...
14
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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 ...
10
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2answers
4k 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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5answers
2k 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 ...
8
votes
4answers
3k 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 ...
3
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1answer
161 views

What to conclude from this lasso plot (glmnet)

Following is the plot of glmnet with default alpha(1, hence lasso) using mtcars data set in R with mpg as the DV and others as ...
5
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6answers
918 views

What machine learning algorithms are good for estimating which features are more important?

I have data with a minimum number of features that don't change, and a few additional features that can change and have a big impact on the outcome. My data-set looks like this: Features are A, B, C ...
3
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1answer
1k views

Feature selection methods for document classtification

I have a simple document classification problem where i need to classify some documents to a definite set of classes. I need to perform a feature selection (where I will select the most important ...
2
votes
1answer
377 views

Using principal component analysis (PCA) for feature selection in regression [duplicate]

I have a dataset $D$ made of $m$ samples and $n$ features with $n \gg m$. For each sample I have a score $s$ which I would like to be able to predict. As the number of features is very high (compared ...
6
votes
1answer
993 views

How to select the final model with elastic net feature selection, cross validation and SVM?

I have a dataset of some 100 samples, each with >10,000 features, some of which highly correlated. Here's what I am doing currently. Split the data set into three folds. For each fold, 2.1 Run ...
5
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3answers
2k views

Selecting the best subset of variables for parsimonious binary logistic regression models

In addition to PROC VARCLUS, randomForest, glmnet, and assessing multicollinearity among potential predictor variables (without regards to the outcome of interest), I am seeking other methods of ...