0
votes
0answers
8 views

Caret: customizing feature selection using matrix-wise operations [migrated]

Short question: is it possible to use matrix-wise operations in caretSBF$score function? Motivation: When working with big matrices in R, operations that work natively matrix-wise [e.g. rowMeans(X) ...
0
votes
1answer
28 views

mtry tuning given by caret higher than the number of predictors

According to this discussion, it seems that the train function of the caret package returns a ...
0
votes
1answer
26 views

Feature Importance in each fold and repeat after repeated cross validation in caret

this is my first post on Cross Validated so I apologize in advance if I'm not yet familiar with any conventions regarding forum posts. Currently, I'm working on a feature selection task using elastic ...
3
votes
2answers
60 views

Why isn't the lasso selected variable even not significant?

I performed a lasso selection using lars::lars for a well normally distributed outcome using a pool of 86 predictors. Here is the plot of the output: The ...
0
votes
0answers
10 views

Boruta score goes to minus infinity

I'm running the Boruta algorithm with a $179\times 36$ predictor matrix and a numerical response. Most of the variables have a score going to -Inf. Should I ...
2
votes
3answers
113 views

Methods in R or Python to perform feature selection in unsupervised learning

What are the available methods/implementation in R/Python to discard/select unimportant/important features in data? My data does not have labels (unsupervised). The data has ~100 features with mixed ...
0
votes
0answers
21 views

Feature selection from wavelet transformation in R

I am new to wavelets. Currently, I am developing a prediction model using time series data. I am using the wavelets package in R. I am taking part of the time ...
0
votes
0answers
59 views

recursive feature elemination in R with caret

i work with R caret software package to select the most important features from some set of data. My response is a factor of multiple classes (e.g. nominal ...
0
votes
2answers
81 views

Best feature selection method for naive Bayes classification

i want to make classification with naive Bayes. I have got about 100 Features. Numerical ones as well as categorical ones. Since i want only the most relevant ones to be included for the ...
1
vote
1answer
104 views

Random forest cross validation for feature selection, imbalanced datasets

I have an 5297X26 imbalanced dataset, the class1 has 588 samples and class2 has 4709 samples. I used the following code to perform random forest: ...
0
votes
0answers
37 views

Dropping predictor variables, based on variable of importance, effect of Random Forest Accuracy

I am trying to use Random Forest to accurately predict forested land cover classes using Landsat 7, climatic and geographical data. I have 23 predictor variables and 1 response variable. When I drop ...
2
votes
2answers
308 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 ...
0
votes
0answers
16 views

Comparing F-values of covariates in R

I'm new here and have a question regarding ANOVA in R. I have an ANOVA table like this from running anova(model) in R, where ...
0
votes
2answers
118 views

R package glmnet: Ridge selects variables

I am using the glmnet package in R. When I set the alpha value = 0, I would expect that no variables are selected. When I look at the coefficients some of them are set to zero. What could be the ...
1
vote
1answer
246 views

Variablity in cv.glmnet results

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

AICc results in R

I used the model: ...
1
vote
1answer
49 views

Does Boruta feature selection (in R) take into account the correlation between variables?

I am a bit of a novice in R and feature selection, and have tried the Boruta package to select (diminish) my number of variables (n= 40). I thought that this method also took into account the possible ...
0
votes
0answers
142 views

How to select a model in quasi-poisson GLM with interactions using drop1 command?

I want to evaluate the effect of three factors (one categorical, and the other two continuous) on the response variable, which is a count data. I have performed 7 candidate GLM models with ...
3
votes
1answer
109 views

Finding interactions using randomForest

I am trying to use randomForest in R to find interaction terms to add to a model. My plan was to fit trees with maxnodes=4 (two ...
0
votes
0answers
18 views

Variable Selection Methods in R [duplicate]

regsubsets and stepAIC are the two most common options for variable selection in R; they can be found in the ...
0
votes
0answers
11 views

Error trying to reduce my data dimentions [duplicate]

I'm trying to produce a linear regression model, but I only have 25 observations and 34 predictors. I'm trying feature selection, ...
4
votes
1answer
140 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 ...
1
vote
0answers
64 views

Classifier predicts only one class

I was trying myself in kaggle CIFAR competition, I trained lots of classifiers but get the same result/fail (don't know how to treat them), maybe someone could help me figure what i'm doing wrong. ...
3
votes
1answer
691 views

Understanding the output of C5.0 classification model using the CARET package

The C5.0 classification model was used in this 4-class problem data with $N_{train}$=165, $P$=11, using caret R-package by ...
2
votes
1answer
232 views

How to interpret this cross-validated sparse LDA figure using CARET package?

Training data with $p$ =11 predictors and $n$ =165 with 4-class problem was cross-validated (5 times repeated 10-fold CV) using the sparse LDA (aka SDA) using caret ...
1
vote
1answer
134 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 ...
4
votes
4answers
2k 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 ...
0
votes
0answers
63 views

Random Forests and Feature selection [duplicate]

First, I split my training set into 10 parts. 9 parts of it, I use as LS and the other one as TS. I now want to do feature selection, so I do feature selection on 9 parts. I use Random Forest to do ...
0
votes
0answers
18 views

Why the maxStages argument in biglars.fit does not work

Why doesn't the biglars.fit function work when maxStages is specified? I've tried multiple values and multiple ways of casting $y$ but it doesn't work. ...
0
votes
0answers
192 views

Metric warning using caret's rfe

I am using the caret package to do feature selection with rfe while training a knn ...
0
votes
0answers
30 views

Recursive Feature Elimination Fails to Output as Expected

Currently I am using rfe function in the "caret" package to do feature selection. There are 380 variables as input candidates. I have done many trials and I noticed that something weird always ...
5
votes
3answers
899 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 ...
1
vote
1answer
93 views

k-fold feature selection

I have a data set with 20 K variables. I have tried to select some features via Boruta and FSelector but I could not achieve ...
0
votes
2answers
888 views

Random Forest: IncNodePurity and Feature Selection for Binary Logistic Regression

After creating a Random Forest object using randomForest with around 500 candidate variables, I used importance(object) to ...
2
votes
1answer
192 views

Combining Exploratory Factor Analysis with Random Forest for Binary Logistic Regression Feature Selection

For those of you familiar with Exploratory Factor Analysis (EFA) and Random Forest (RF), I have recently had an idea of combining these two methods to reduce the number of potential predictor ...
0
votes
1answer
293 views

Exploratory Factor Analysis for Binary Logistic Regression Variable Selection

I have a great interest in learning new methods(at least to me) of variable selection in regards to binary logistic regression when I am working with over 500 potential predictor variables and have ...
3
votes
1answer
271 views

Using Mutual Information for Binary Logistic Regression Variable Selection

In addition to proc varclus, randomForest, and assessing multicollinearity among potential predictor variables, I am seeking ...
1
vote
1answer
128 views

How to check the features which are selected by LASSO

I am using LASSO (glmnet) to do feature selection. However, how can I check which features are selected?
5
votes
2answers
2k 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 ...
1
vote
1answer
418 views

Not all Features Selected by GLMNET Considered Signficant by GLM (Logistic Regression)

I wanted to create a predictive model of mortality after patients had undergone a surgical procedure. But I also wanted to avoid doing what most researchers do by first performing univariate analysis ...
4
votes
0answers
75 views

Which variables are driving correlations within groups

I'm running an analysis on a few data sets that each typically have 100-200 cases measured across 120-160 variables - something similar to looking at gene expressions. Each variable is a non-centered ...
3
votes
1answer
447 views

Can we use random forest for classification in combination with distance matrix between classes?

With a colleague, we are working on a dataset containing ~5000 continuous variables for 120 individuals belonging to 8 classes. We want to estimate the relative importance of each variable to explain ...
4
votes
1answer
375 views

Select best set of binary variables for clustering known sample labels

I have a set of samples, for which I know the "true groups". For this samples I have about 200 binary variables, I would like to know a method to select the subset of variables, that gives me a ...
3
votes
0answers
340 views

Variable selection / Dataset reduction for large datasets (in R)

I'm working on a behavoural scorecard modelling exercise, and many of the decisions taken to date have been based on the experience of a consulting credit analyst (whose experience software-wise is ...
1
vote
0answers
107 views

Using MatchIt to match groups in a retrospective analysis

I am interested in using the R package MatchIt to preprocess my data as to obtain matched groups based on a predefined treatment variable. However I am facing a few issues. The first issue is that ...
3
votes
3answers
5k views

Feature Selection Packages in R

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
vote
1answer
202 views

Choosing one variable from each of 3 buckets of variables

I have a regression model that looks like the following glm.nb(formula = y ~ Gender + Age + x1 + x2 + x3, data = df) In my problem, there are 20 possible choices ...
0
votes
1answer
162 views

Relative importance weight with cforest

I am new in using RF. I want to use it to compute the relative importance of the features. I found the weight is very small ("party" package, cforest). Is there anyway to get these weights in a range ...
1
vote
2answers
617 views

Alternatives to glmnet for feature selection on data with lots of NAs

I have a surgical database in which there are approximately 100,000 observations and 200 features. Each observation corresponds to a separate patient while the features correspond to either ...