Tagged Questions
1
vote
1answer
72 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
21 views
Strategy for building best fit multiple regression model with time lagged variables
I am building a multiple regression model - wrapped in a function - with one dependent variable and a dozen independent variables. The reason why I am building a function is that I need to do this ...
2
votes
0answers
37 views
SVM classifier (with soft-margin) implementation in R, gamma value and quadprog
I'm trying to implement a Support Vector Machine classifier in R and I have to solve the optimization problem using the quadprog R package which solves problems of the form :
$$min_b \frac{1}{2} ...
0
votes
2answers
47 views
Time Series Similarity : Differing Lengths with R
I am experimenting with creating a distance matrix between time series for clustering and similarity searching. The main reference I am using is for the Similarity procedure in SAS (Paper). I would ...
0
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1answer
86 views
0
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0answers
20 views
How does R{MASS} lda function use MLEs to improve its result?
I am using the LDA function in the MASS package of R, which has the following specification:
...
0
votes
0answers
44 views
Energy estimation through machine learning
Greedings to everybody.
I have the dataset which you can find here, containing many different characteristics of different houses, including their types of heating, or the number of adults and ...
1
vote
0answers
51 views
Deciding attributes for decision trees
I'm a complete beginner when it comes to R and decision trees, but I was asked to take a look at this to see if this was a viable solution for my data. So please excuse me if I say completely wrong.
...
0
votes
1answer
69 views
Adding noise to a column of data
I am working with this optdigits data set from UCI machine learning repository and want to create a new training dataset with noise. How to randomly add noise to a vector in R? say corrupt 10% of the ...
2
votes
0answers
46 views
How should I distribute a classifier to customers?
When consulting, I often do my exploratory analysis and prototyping in R, and deliver results on the initial dataset to the client. The client wants to use the trained classifier in a production ...
1
vote
1answer
54 views
Interpretation of Scree plots and Boruta Outcomes
I have 37 features in my dataset. I used Boruta package in R and according to its analysis, all the features are "important" and should be retained. I examined this result of Boruta and found that if ...
0
votes
0answers
83 views
Modeling using neuralnet package in R - lots of issues
I am using neural net package in R. While I understand the basic neural network concepts, the details and back end is still a tough nut for me.
Currently all I can do is use brute force to change ...
0
votes
0answers
78 views
Description of all models in R's caret package? [closed]
I've been looking into machine learning recently, mostly using R. I just came across the caret package and it seems to be brilliant for quickly trying out different models. It seems a great tool for ...
1
vote
0answers
178 views
Machine Learning Algorithms vs. Linear Regression
Do machine learning algorithms like Boosted Regression Trees (in the R package (gbm)) follow the same statistical assumptions of not including correlated predictor variables in GLM?
i.e. If I have ...
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
175 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 ...
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" ...
6
votes
1answer
1k views
R vs Python for Data Analysis [duplicate]
Possible Duplicate:
Python as a statistics workbench
I am just starting out with data analysis and machine learning. From the books that I am reading/have read Python and R seem to be the ...
1
vote
1answer
641 views
Example of time series prediction using neural networks in R
Anyone's got a quick short educational example how to use Neural Networks (nnet in R for example) for the purpose of prediction?
Here is an example, in R, of a time series
...
0
votes
0answers
51 views
Can RBMs be used for feature selection / reduction?
I have a data set that's ~ 150R X 2000C and was curious if an RBM is appropriate in situation with this type of imbalance. It's a microarray and I'm looking at a 0/1 classification problem. I'd be ...
1
vote
1answer
180 views
Meaning of output terms in gbm package?
I am using gbm package for classification. As expected, the results is good. But I am trying to understand the output of the classifier.
There are five terms in output.
...
1
vote
1answer
231 views
Looking for examples or alternatives to R RuleFit ensemble package
Does anyone know of any good example code illustrations for the rulefit Rule Based Learning Ensembles package? The documentation is incredibly lacking. I was guided to the package by this paper.
If ...
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
0answers
80 views
Regarding the sampling procedure in Adaboost algorithm
The AdaBoost algorithm states that it is to train a classifier based on the training data according to a weight vector.
Assume the size of training data is N, the weight vector is of dimension N as ...
0
votes
1answer
260 views
R neural net training and prediction
I am trying to form a model on a set of data that I gathered from MT4. The OHLC and some MA slopes.
I am trying to get the best guess for price change in the future.
I am using
...
3
votes
1answer
141 views
In caret what is the real difference between cv and repeatedcv?
This is similar to question Caret re-sampling methods, although that really never answered this part of the question in an agreed upon way.
caret's train function offers ...
1
vote
4answers
271 views
Methods & CRAN packages to predict probability using neural networks or others machine learning algorithms
I have a medical database containing 7 input variables (4 are binary) and a binary outcome variable (Survival: yes/no). My objective is to train and test an algorithm that predict probability of ...
0
votes
1answer
101 views
How do I access or compute the posterior covariance matrix returned by kernlab::gausspr R function?
I am looking to compute the covariance matrix of an inferred Gaussian process in R. Below I outline how I would do this manually, but I realize that the kernlab ...
0
votes
2answers
65 views
logistic regression always yielding increasing f'n when should sometimes be decreasing (using R)
I'm modeling a set of outcome data the depends on two parameters:
time, T
-100 < A < 100
I've done logistic regression using R with the command:
...
0
votes
0answers
50 views
Test effect of discretization procedure on classification performance using ANOVA
I examine the effect of discretization on classification performance. Assessment procedure is as following:
Discretize dataset $D$ using discretization algorithm $A$, where $A \in \{ \text{ef}, ...
2
votes
1answer
110 views
R packages or open source software for training Hidden Markov chains
Are there any well-designed R packages or other open-source software for training Hidden Markov chains?
4
votes
2answers
487 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 ...
12
votes
2answers
1k views
Restricted Boltzmann machines vs multilayer neural networks
I've been wanting to experiment with a neural network for a classification problem that I'm facing. I ran into papers that talk of RBMs. But from what I can understand, they are no different from ...
2
votes
0answers
70 views
Notation in GBM package vignette: expected value of loss functions
Can anyone help with the understanding of this notation (and idea) from the vignette for GBM in R?
It starts with the following:
Question 1: I believe this is simply saying that we are looking for ...
0
votes
0answers
167 views
Best platform for running (python and ( R or Octave)) algorithms for (large/big) data analytics [closed]
I have a machine learning algorithm currently implemented in R, wrapped in python (rpy2).
I would like to deploy this inside a web application and I am looking for the right platform to do this, ...
0
votes
1answer
183 views
Trying to run statistical tests in R but struggling as I am new to the language
Good Day, I believe this issue is more of a lack of understand of R (as I have never used it till recently) than anything else. What I am looking for is references, or documents to help me solve my ...
0
votes
1answer
115 views
R package for feature set algorithm selection
I want to train a binary classification NN and part of this will require data pre-processing. However, I have a choice of which pre-processing algorithm to use. Of course I'd like to choose that one ...
3
votes
1answer
354 views
Naive Bayes fails with a perfect predictor
Let's say I have a variable that perfectly predicts one of the classes in my dataset:
...
1
vote
1answer
188 views
What are the rules / guidelines for downsampling?
I have a data set with ~ 7 million rows, of which ~ 100k are positives. I'm looking to shrink the data by keeping all the ...
2
votes
0answers
176 views
Genetic algorithms, genetic programming or machine learning algorithms for solving this problem
I have a problem that consists of finding the optimal solution based on the following criteria:
Logic for identifying that event A has occurred (i.e. "find" logic that most accurately categorises an ...
1
vote
1answer
148 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 ...
2
votes
1answer
68 views
Is a different CV arrangement the same as a validation set?
I have a smallish dataset ~ 1500 rows X 500 columns. I've been using a standard 5 fold CV setup where row 1 = CV set1, row2 = CV set2, ... row 6 = CV set1,etc.
I'm at the point where I'm trying to ...
2
votes
1answer
468 views
k-fold cross-validation strategy for large data set in statistical learning
I'm trying to learn the Bayesian network structure from a very large data set, and the R package I used for learning can only handle a very small portion of the data set (~10%) at one time due to the ...
1
vote
2answers
140 views
Getting started with R
I have access to a database with a lot of credit data about individuals through my job and I'd like to use it to learn some R and eventually, come up with some models that predict credit worthiness ...
8
votes
2answers
829 views
Why do Lars and Glmnet give different solutions for the Lasso problem?
I want to better understand the R packages Lars and Glmnet, which are used to solve the Lasso problem:
$$min_{(\beta_0 \beta) ...
0
votes
0answers
49 views
Generating a quality score
Let's say that I am buying something, let's say it's information on consumers. I am bidding on an item and could win or not win. If I win an item, and realize that what I purchased was of poor ...
3
votes
0answers
268 views
Classification with GBM in R and imbalanced class sizes
I'm dealing with a supervised binary classification issue. I'd like to use the GBM package to classify individuals as uninfected/infected. I have 15 times more uninfected than infected individuals.
I ...
3
votes
1answer
143 views
Predictive models with large numbers of missing values in the features
I have been trying to train an algorithm to predict if an account will close or not using thousands of data points and many features.
I am using data from the month before the account closed but ...
2
votes
0answers
165 views
Efficient Portfolio Optimization Through Simulation
Apologies in advance for the (possibly?) poor terminology as I'm a bit of a novice in the field. I was torn whether to ask this on stackoverflow or here, so hope its the right place.
Anyway, my ...
