Tagged Questions

2
votes
2answers
72 views

What machine learning techniques can, once trained, generate prediction despite some missing inputs?

I have a training set where the inputs & outputs are all present, but I suspect that in the data where I want to do prediction, I will occasionally encounter scenarios where a small fraction of ...
0
votes
0answers
44 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 ...
5
votes
2answers
78 views

Why do categorical predictor variables in regression need to be recoded as multiple predictors?

I'm learning about machine learning using Python's library scikit learn, and in their tutorial here they mentioned about a categorical variable color which can have ...
0
votes
1answer
64 views

Why is Hedonic Regression used instead of Linear Regression

Why is Hedonic Regression used (especially in housing prices) instead of Linear Regression? There do not seem to be any libraries in Python (and R) for Hedonic regression, is it too niched a ...
1
vote
4answers
125 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
1answer
54 views

What exactly is the equation for SVM classification for new example?

I understand that in the case of Logistic Regression, we simply multiply our weights with Input example for classification. But what exactly is the equation that we calculate in the case of SVM to ...
6
votes
2answers
103 views

Is there overfitting in this modellng approach

I recently was told that the process I followed (component of a MS Thesis) could be seen as over-fitting. I am looking to get a better understanding of this and see if others agree. The objective of ...
4
votes
2answers
71 views

Predicting chemical property (Boiling Point) from a SMILES string

I was trying to develop a model for predicting Boiling Points (BP) given a chemical name. One good and unique (ok, almost) way to encode a name is the SMILES notation string. The details of the ...
0
votes
0answers
42 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 ...
1
vote
0answers
39 views

Prediction with intervals as the independent variable

I have sample data that maps intervals to a number: [3,7] => 1 [6,8] => 2 [6,13] => 3 [7,10] => 3 [10,13] => 4 The dependent variable's values ...
2
votes
1answer
142 views

Predicting Football match winners based only on previous data of same match

I'm a huge football(soccer) fan and interested in Machine Learning too. As a project for my ML course I'm trying to build a model that would predict the chance of winning for the home team, given the ...
3
votes
0answers
92 views

Shifted intercepts in logistic regression

I have a question about the effects of shifting the intercept in a logistic fit on the mean of a particular transformation of the scores. Here is the notation I will be using for the question. The ...
7
votes
5answers
238 views

Does preclustering help to build a better predictive model?

For the task of churn modelling I was considering: Compute k clusters for the data Build k models for each cluster individually. The rationale for that is,that there is nothing to prove, that the ...
3
votes
0answers
273 views

High-dimensional Regression Datasets [closed]

Am looking for pointers to publicly(online) available high-dimensional regression datasets for evaluating my research work. By high-dimensional, am looking for regression datsets with the number of ...
1
vote
1answer
85 views

Robust regularized regression

I've been using elastic net implemented in R (via glmnet) for some modeling, but I was wondering, due to the number of outliers in my data, if there was some sort of modeling approach for regularized ...
2
votes
1answer
69 views

Effect of varying outcome duration in longitudinal studies

I have a supervised classifier model (regularized discriminant) which predicts the probability of an event occurring within two years. This model was developed using sensor data measured from a ...
1
vote
0answers
46 views

Distance correlation and prediction

If the distance correlation (ref. Gabor J. Szekely) $R_n(X,Y)>R_n(Z,Y)$ would the expected generalization error of a prediction model over $(Z,Y)$ be lower than $(X,Y)$ in predicting $Y$, where ...
1
vote
0answers
133 views

Gaussian process - dimensionality reduction

Specific question on Gaussian Processes and dimensionality reduction. I saw a a method for dimensionality reduction for the squared exponential covariance function (not ARD) whereby one uses a GxD ...
0
votes
1answer
510 views

Gaussian Process regression for high dimensional data sets

Just wanted to see if anyone has any experience applying Gaussian process regression (GPR) to high dimensional data sets. I'm looking into some of the various sparse GPR methods (e.g. sparse ...
1
vote
0answers
133 views

Guassian Process Regression - feature selection

I'm using guassian process regression to do some modeling. One issue I'm encountering is feature selection for some of my models, which often have many relevant features. I'm not sure what the best ...
3
votes
2answers
680 views

Why does Lasso do better than SVM?

This is a soft-question: I have been evaluation various regression techniques over a regression dataset that I have. I am surprised by the fact that cross-validated RMSE of Lasso is better than SVM ...
1
vote
0answers
122 views

SVM and non-linear predictive models - feature selection

Just throwing out a general question. What do people think of applying feature selection methods when using SVMs to build predictive models? I understand that SVM have built in regularization with how ...
1
vote
0answers
101 views

Robust Support Vector Regression - robust to outliers

I've been reading/looking around for literature on support vector regressions that are relatively robust to outliers. I understand that standard SVRs can be significantly influenced by a few large ...
4
votes
2answers
394 views

Support vector regression on skewed/high kurtosis data

I'm using support vector regression to model some fairly skewed data (with high kurtosis). I've tried modeling the data directly but I'm getting erroneous predictions I think mainly due to the ...
4
votes
1answer
153 views

Incorporating a treatment into a classification scheme

I have about 400 pieces of silver of different geometric dimensions. They were assigned to six groups and each group went through a series of stress tests, such as bending, pulling, putting in fire ...
1
vote
1answer
138 views

SVM parameter selection with NM simplex (or other algorithms)

I'm having some trouble getting the NM Simplex to find a good minimum for selecting hyperparameters of a rbf SVC. Not only am I tuning the 2 SVC parameters (C and gamma) I also have five class weights ...
2
votes
3answers
115 views

Multi task learning

I have a dataset where all observations are measured several times and reported outcomes correspond to those measurements. In other words, my set of data points looks like $\{x_i, y_{i_1}, y_{i_2}, ...
3
votes
0answers
58 views

Graphical nominal model

Suppose I have a set of $k$ matrices. $$ \epsilon = A_1,A_2,...,A_k $$ Each column of $A$ is categorical vector. $$ A = v_1,v_2,...,v_n $$ I want to find the mapping $$ f: A ...
1
vote
1answer
158 views

Non-linear regularized SVM implementation

Just a general question. Are there any good non-linear SVM (kernelized) implementations that include a regularization component (e.g. $L_1$, SCAD etc)? I've been looking around but man there are a lot ...
2
votes
0answers
81 views

Non-linear (e.g. RBF kernel) SVM with SCAD penalties implementation

Is there one? I think there's a penalizedSVM package in R but it looks to use a linear kernel. Can't quite tell from the documentation. If it's linear, is there a R package that lets me calculate the ...
2
votes
1answer
79 views

General Non-linear Regularized Models

Had a general questions. Are there any good non-linear models with regularization? I've heard of some linear models with regularization but not too many non-linear ones. I understand that you can use ...
2
votes
1answer
676 views

Why does GBM predict different values for the same data

I am new to R. I am building predictive model with gbm package. I have a problem that I retrieve different results for data from data frame that was used for building of the model and for separate ...
1
vote
1answer
587 views

Fastest SVM implementation

More of a general question. I'm running a rbf SVM for predictive modeling. I think my current program definitely needs a bit of a speed up. I use scikits learn with a coarse to fine grid search + ...
2
votes
1answer
204 views

Multiclass SVM + Ineffective X Validation, Time Series Prediction

I've recently run into an interesting and rather odd problem with cross validating a multiclass SVM that I can't figure out. Basically, I have a timeseries to predict and have created a dataset of ...
2
votes
0answers
316 views

Cross validation procedure - is this right?

Just want to check that I am performing my cross validation procedures right. I'm using a non-linear svm. I do a five fold cross validation (5 splits of test/train on my original training data) and ...
2
votes
1answer
122 views

SVM and cross validation with a minimum finding algorithm

Just a simple question on parameter selection for SVMs. If I use a minimum finding algorithm to find the optimal parameters for a set of data, how do I "average" the parameters over a set of cross ...
2
votes
2answers
602 views

SVM parameter selection and cross validation

Have a quick question about parameter selection for an SVM. I'm using a rbf kernel, so trying to optimize C and gamma. I have an example set of around 4500, about 700 features, and using 700 examples ...
9
votes
1answer
259 views

Predictive Modeling - Should we care about mixed modeling?

For predictive modeling, do we need to concern ourselves with statistical concepts such as random effects and non independence of observations (repeated measures)? For example.... I have data from 5 ...
4
votes
2answers
189 views

Predicting multiple targets or classes?

Suppose I am building a predictive model where I am trying to predict multiple events (for instance, both the roll of a die and the toss of a coin). Most algorithms that I am familiar with work with ...
2
votes
3answers
114 views

Why are discriminative models called 'discriminative'?

The name discriminative leads to some inherent definitions that I don't think are true. When first told of discriminative models, intuition says "It is a model that improves training accuracy between ...
3
votes
2answers
332 views

SVM, variable interaction and training data fit

I have 2 general/more theoretical question. 1) I'm curious how SVMs handle variable interactions when building predictive models. E.g., if I have two features f1 and f2 and the target depends on f1, ...
1
vote
1answer
1k views

Best way to handle unbalanced multiclass dataset with SVM

I'm trying to build a prediction model with SVMs on fairly unbalanced data. My labels/output have three classes, positive, neutral and negative. I would say the positive example makes about 10 - 20% ...
18
votes
7answers
461 views

How can I help ensure testing data does not leak into training data?

Suppose we have someone building a predictive model, but that someone is not necessarily well-versed in proper statistical or machine learning principles. Maybe we are helping that person as they are ...
2
votes
1answer
181 views

Weights of radial basis function networks

If I use radial basis function networks (RBFNs) for probability estimation by plugging the output of the RBFNs into the Logistic function are weights between 0 and 1 sufficient?
5
votes
2answers
585 views

Building background for machine learning for CS student

I am a CS graduate student and I am starting to get really interested in Machine Learning (and Predictive Analytics). I have started working on a text classification project with a professor to learn ...
1
vote
1answer
120 views

Algorithms for predicting a couple points in the future

I'm familiar with supervised learning algorithms like regression and neural networks which look at a bunch of input points and learn a function which outputs a value (the value varying depending on ...
2
votes
0answers
80 views

Predictive model for network data

Assume a network as a set of data, which are defined by their coordination $(x,y,z)$ and a weight on its edge. Now this data can be used as an input data to predict a single value. In my case, ...
4
votes
2answers
114 views

Is there a generic term for measures of correctness like “precision” and “recall”?

Suppose I am building some predictive models and then creating a report detailing how "good" those models were in various ways. Is there a generic (maybe even non-technical) term for the various ...
7
votes
2answers
396 views

Machine learning techniques for time series estimation - forecasting price

Can anyone recommend any machine learning techniques for time series estimation? I have a series of times $t_{1}...t_{n}$, each having a set of associated features $f_{1}...f_{m}$, and a value $x$. ...
12
votes
2answers
576 views

Sites for predictive modeling competitions

I participate in predictive modeling competitions on Kaggle, TunedIt, and CrowdAnalytix. I find that these sites are a good way to "work-out" for statistics/machine learning. Are there any other ...

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