Tagged Questions

0
votes
1answer
68 views

First steps learning to predict financial timeseries using machine learning

I am trying to get a grasp on how to use machine learning to predict financial timeseries 1 or more steps into the future. I have a financial timeseries with some descriptive data and I would like to ...
0
votes
0answers
35 views

Appropriate method for supervised learning of small data set with few variables

What method exc. for regression can be used in order to get y=f(x1,x2) on a training set of 800 to 2000 samples? y is a whole number <0,15>, x1,x2 are real <0,40>? I'm interested in prediction ...
0
votes
0answers
47 views

When to Log/Exp your Variables when performing Linear Regression?

I'm doing regression using Random Forests for predicting prices based on several attributes. Code is written in Python using Scikit-learn. How do you decide whether you should transform your ...
1
vote
0answers
37 views

Which Regression methods are suitable for binary valued features and continuous output?

I want to build a machine learning model to regression on continuous output given binary valued features(0,1). the dimension of my problem is around 200. which of the flowing methods seems suitable ...
1
vote
4answers
126 views

Measuring representativeness of a sample using covariates

I was provided with quite a small sample of labeled (variable of interest) observations to train a model to predict unlabeled observations. All the observations are associated with many covariates. ...
2
votes
0answers
31 views

Maximum number of classes for RandomForest multiclass estimation

I have researched the internet|literature a lot on multiclass prediction to find out what is a realistic limit for the number of classes that can successfully be used for estimation when using a ...
0
votes
0answers
43 views

Weighting and time series with machine learning

I'm trying to produce a model to predict the price of a product on the basis of several factors effecting previous time-stamped sales. I am certain that older sales are less relevant to the prediction ...
3
votes
1answer
65 views

Variable importance randomForest negative values

I am asking myself if it is a good idea to remove those variables with a negative variable importance value ("%IncMSE") in a regression context. And if it gives me a better prediction? What do you ...
1
vote
1answer
60 views

Non-independence of IVs in a random forest model

How is a random forest model affected if some of the variables are not independent?
2
votes
0answers
72 views

Random Forest or other machine learning techniques with paired samples

I am trying to select features from paired samples and was wondering if there are methods out there that adapt random Forest or other machine learning algorithms to paired samples i.e. if randomForest ...
2
votes
1answer
107 views

Building the dataset for Random Forest training procedure

I should use the bagging (bootstrap aggregating) technique in order to train a random forest classifier. I read here the description of this learning technique, but I have not figured out how I ...
0
votes
0answers
80 views

Missing measure of variable importance for randomForest package in R [closed]

I have a problem with the importance measures given by the randomForest package in R. I pass the parameter importance=TRUE to my ...
0
votes
0answers
104 views

Random Forest output inerpretation

I am trying to do an profiling exercise with the help of random Forest. Attached is the code and sample data. How can I know what are variables used in tree (how can I get the profiles as output based ...
2
votes
1answer
174 views

Caret and randomForest number of trees

I am puzzled as to why the caret package in R does not allow tuning on the number of trees (ntree) in a random forest (specifically in the randomForest package)? I cant imagine this is an oversight on ...
2
votes
1answer
235 views

PCA before random forest classification

Does it make sense to do PCA before carrying out a Random Forest Classification? I'm dealing with high dimensional text data, and I want to do feature reduction to help avoid the curse of ...
3
votes
1answer
140 views

How does extreme random forest differ from random forest?

Are they more efficient implementation -- is the difference important from practical point of view, there is R package which implements them. Is it new algorithm which overcomes "generic" ...
3
votes
2answers
322 views

How to combine results of logistic regression and random forest?

I am new to machine learning. I applied logistic regression and random forest on a same dataset. So I get variable importance (absolute coefficient for logistic regression and variable importance for ...
3
votes
0answers
72 views

Using taxonomic levels as factors in random forests: does it make sense? Is it needed?

I want to test the effect of a set of predictors (ecological and morphological factors) on a categorical response variable (an animal behaviour). As far as I've read, random forests do not make ...
4
votes
2answers
486 views

Number of trees for Random Forest optimization using recursive feature elimination

How many trees would you suggest to pick to perform recursive feature elimination (RFE) in order to optimize Random Forest classifier (for binary classification problem). My dataset is very ...
4
votes
1answer
1k views

Does the optimal number of trees in a random forest depend on the number of predictors?

Can someone explain why we need a large number of trees in random forest when the number of predictors is large? How can we determine the optimal number of trees?
2
votes
0answers
49 views

General rules for choosing machine-learning algorithm? [duplicate]

Possible Duplicate: Machine learning cookbook / reference card / cheatsheet? There are numerous machine-learning approaches out there. Also there are numerous ways how to optimize their ...
3
votes
1answer
268 views

Why do I need bag composition to calculate OOB error of combined random forest model?

Could someone please explain me the answer of the question already given here: Combining randomForests in R, why are the err.rate, mse and rsq components NULL I do not understand the phrase "clip ...
1
vote
1answer
147 views

Recommendations for MRI classification in R of large dataset (n=100, p=20000)

I am working on a magnetic-resonance imaging dataset which includes about 100 observations (= subjects) and 20000 predictors (=voxels). I would like to conduct classification in R using methods like ...
8
votes
1answer
201 views

Stratified classification with random forests (or another classifier)

So, I've got a matrix of about 60 x 1000. I'm looking at it as 60 objects with 1000 features; the 60 objects are grouped into 3 classes (a,b,c). 20 objects in each class, and we know the true ...
4
votes
1answer
120 views

Estimating the variance of a bootstrap aggregator performance?

When performing cross-validation or bootstrap re sampling to estimate the performance of some machine learning algorithm, one commonly records the mean and variance of the errors obtained in of all ...
5
votes
2answers
527 views

How to make Random Forests more interpretable?

Are there any methods that one could utilize to make Random Forest more interpretable? Random Forest performs much better than CART but it is a lot less interpretable.
3
votes
0answers
257 views

Post processing random forests using regularised regression: what about bias?

I have been playing around with post processing the results of the random forest for regression machine learning algorithm in order to try and do better than the default mean of all trees prediction. ...
4
votes
0answers
313 views

Would a Random Forest with multiple outputs be possible/practical?

Random Forests (RFs) is a competitive data modeling/mining method. An RF model has one output -- the output/prediction variable. The naive approach to modeling multiple outputs with RFs would be to ...
2
votes
2answers
203 views

Why adding an NA indicator column instead of value imputation (for randomForest)

I have downloaded the sample codes from kaggle for the randomForest benchmark [URL?] and there's this part that I don't understand. ...
1
vote
1answer
306 views

Modification of "corrected repeated k-fold cv test” when also averaging Random Forest results across multiple sampling seeds?

I would be very grateful for any ideas concerning the following problem. I would be even more grateful if someone could point me to a literature reference proposing a solution for a scenario like the ...
1
vote
3answers
328 views

How to implement a customized Random Forest classifier

I am trying to replicate an extension of Random Forests introduced in a recent research publication for my project. For instance, for the binary split of data at each node, instead of randomly ...
7
votes
1answer
648 views

How does the Kinect use random forests?

I read on this site that apparently the Kinect uses the random forests algorithm for machine learning in some way. Can anyone explain what it uses random forests for, and how their approach works?
2
votes
1answer
259 views

Custom error function for randomForest R package

I am a R beginner. Is there a way to specify custom error function with Random Forests in R? For example, say my training data is ,,, so on and my error for any given set needs to normalize the ...
4
votes
2answers
237 views

How are classifications merged in an ensemble classifier?

How does an ensemble classifier merge the predictions of its constituent classifiers? I'm having difficulty finding a clear description. In some code examples I've found, the ensemble just averages ...
1
vote
0answers
310 views

How do I report error from imbalanced data in a random forest algorithm?

I have built what I think is a very good predictive model using randomforest. The initial dataset was imbalanced for the outcome 2:1, so I randomly resampled the dataset to balance it, then trimmed ...
0
votes
1answer
150 views

Sampling to get around randomForest's 32 factor level limit

I'm trying to work around the randomForest package limit of 32 levels for factors. I have a data set with 100 factors in one of the variables. I wrote the following code to see what things would ...
1
vote
1answer
683 views

How do you calculate variable importance p-values using the randomForest package in R?

For a classification project we are using the randomForest package in R, which wraps the Breiman Fortran random forest implementation, to assess the importance of each of our features. I would like to ...
15
votes
3answers
2k views

How well does R scale to text classification tasks?

I am trying to get upto speed with R. I eventually want to use R libraries for doing text classification. I was just wondering what people's experiences are with regard to R's scalability when it ...
4
votes
4answers
1k views

Compare R-squared from two different Random Forest models

I'm using the randomForest package in R to develop a random forest model to try to explain a continuous outcome in a "wide" dataset with more predictors than samples. Specifically, I'm fitting one RF ...
2
votes
1answer
1k views

Measures of variable importance in random forests

I've been playing around with random forests for regression and am having difficulty working out exactly what the two measures of importance mean, and how they should be interpreted. The ...
12
votes
3answers
2k views

Creating a “certainty score” from the votes in random forests?

I am looking to train a classifier that will discriminate between Type A and Type B objects with a reasonably large training set ...
8
votes
3answers
3k views

For classification with Random Forests in R, how should one adjust for imbalanced class sizes?

I am exploring different classification methods for a project I am working on, and am interested in trying Random Forests. I am trying to educate myself as I go along, and would appreciate any help ...
9
votes
1answer
713 views

Is there a way to explain a prediction from a random forest model?

Say I've got a predictive classification model based on a random forest (using the randomForest package in R). I'd like to set it up so that end-users can specify an item to generate a prediction for, ...
5
votes
3answers
1k views

What should be the optimal parameters for Random Forest classifier?

Currently i am using RF toolbox on MATLAB for a binary classification Problem Data Set: 50000 samples and more than 250 features So what should be the number of trees and randomly selected feature ...
5
votes
1answer
1k views

cforest and randomForest classification prediction error

I used cforest and randomForest for a 300 rows and 9 columns dataset and received good (almost overfitted - error equal to zero) results for randomForest and big prediction errors for cforest ...
3
votes
1answer
706 views

How to assess overfitting?

This is a follow-up of the question I posted earlier. I am assessing the two RF models which are generated using two different set of features NF - Test_Accuracy > Training accuracy (500 ...
4
votes
2answers
663 views

Statistical validation of RandomForest models

I am currently working on a RandomForest based prediction method using protein sequence data. I have generated two models first model (NF) using standard set of features and the second model (HF) ...
8
votes
1answer
1k views

Why does the random forest OOB estimate of error improve when the number of features selected are decreased?

I am applying a random forest algorithm as a classifier on a microarray dataset which are split into two known groups with 1000s of features. After the initial run I look at the importance of the ...
22
votes
3answers
7k views

Best way to present a random forest in a publication?

I am using the random forest algorithm as a robust classifier of two groups in a microarray study with 1000s of features. What is the best way to present the random forest so that there is enough ...
9
votes
2answers
1k views

How does random forest generate the random forest

I am not an expert of random forest but I clearly understand that the key issue with random forest is the (random) tree generation. Can you explain me how the trees are generated? (i.e. What is the ...