1
vote
0answers
19 views

Hard Case - prediction of chain stores revenue

Data about average monthly revenue from 2000 stores around whole country. Gini coeff. of reve around 20%, with 50% of observation around average, very thin tails of distribution Explanatory ...
4
votes
4answers
177 views

Predicting time to finish

Out of curiosity, I want to understand how to model this problem. I've been hearing people suggest the use of linear regression but I am not sure how to encode this problem (included my attempt below) ...
0
votes
0answers
20 views

GBM, it's overfitting/multicollinearity problem and parameter setting up

I recently came across a predicting problem (0-1 outcome, with more than 80 variables), I decided to use GBM (Gradient Boosting Machine by Friedman)to handle this job. I let the GBM use only 70% of ...
3
votes
2answers
37 views

What model would be appropriate for predicting electrical consumption given multiple (mostly) independent variables?

I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including: daily temperature (continuous) hours ...
1
vote
0answers
22 views

What are some multivariate models with feature interactions

I have dependent variable matrix $Y_{i,j}$ and feature matrix $X_{i,k}$. My objective is to predict each element of the vector $[y_{i,0},...,y_{i,J}]$ by using new observations of the features, ...
0
votes
0answers
43 views

Machine learning from implied variables

I have a situation where we are detecting anomalies based on data implied from the table data. As an example, I have data on registered individuals spending time on the portal. Based on this, I have ...
2
votes
1answer
136 views

Predictive Modeler: How can learning Python and/or Java benefit me?

On a daily basis, I build predictive models (namely, logistic regression and credit scorecard models) using fairly large datasets (typically ~500k records and ~1k candidate variables) to predict ...
0
votes
1answer
156 views

Choosing correct C and g parameters for libsvm

libsvm 3.18 Features: 10 I have used following, parameter range: ...
0
votes
1answer
59 views

Improving classification results

I have training data set with around 1500 positive set samples and 4500 negative set samples. All the features are numeric( floating or integer type values) and the data is specific to bio-informatics ...
1
vote
0answers
46 views

Random Forest and Factor Predictors [duplicate]

How do decision tree based ensembles like random forest deal with categorical ("factor") predictor variables? My guess would be that indicator variables are created for each factor via a ...
0
votes
0answers
51 views

Data Mining study and prediction of a Dataframe in R

I'm new in the Data Mining World. I have a Dataset of 19 variables(some of them categorical). It is about execution time of different aplicattions. I have something like this) but with thousands of ...
1
vote
1answer
70 views

Quantifying applicability domain for predictive models?

I have a big dataset and I want to build a classification model (svm, rf, ann etc.). Then I split the original dataset into training set and test set. I build the model using training set. After it ...
1
vote
1answer
69 views

Computational Complexity of Prediction using SVM and NN?

I've seen answers discussing the complexity of training SVMs and neural nets, but how about for predicting new responses once a model has been trained? For context, I'm working on an app that should ...
1
vote
1answer
65 views

Hidden Markov Model: Predict observation sequence from state sequence

Given a transition matrix, starting probability, means and covariances Is it possible to predict the most likely observed sequence for a given state sequence using the above details? If yes, how? ...
0
votes
0answers
30 views

Literature for prediction models where each training example has a different amount of data?

This could be a machine learning question as much as a statistics question, but I think this is the best place to put the question. Here are three different examples of problems where each ...
0
votes
0answers
66 views

Time Period Predictive Modeling

I have been implementing some classification algorithms (Naive Bayes, SVM etc) recently on the iris data sets to get head start into the data science field. I enjoy working on machine learning ...
0
votes
0answers
31 views

Proper way to determine attribute feature selection's smaller subset based on result metrics

Overview My goal is to predict survival of an instance for five different time periods (binary attribute). I have a 100,000-instance dataset with 40 attributes and I want to reduce the attributes ...
1
vote
1answer
1k views

Interpretation of a WEKA result buffer - confusion matrix and performance

I want to know how to get several performance measurements of a generated WEKA model. Note that I am predicting a two-class variable, Alive or ...
8
votes
2answers
153 views

Coupling time series information from sources with multiple spatial resolutions/scales

I have many satellite raster images available from different sensors. From these, the coarser ones have a very abundant temporal resolution. The medium resolution rasters tend to have less acquisition ...
0
votes
0answers
45 views

Machine Learning techniques for classifying response from several continuous factors

I have some data with rows corresponding to male-female pairs, with 8-10 continuous factors (that have been unfortunately anonymized) that apply to both the male and female, and whether they became ...
1
vote
1answer
154 views

Learning curve shows decreasing accuracy

I'm working on a random forest classifier with 10-folds CV to aestimate the hyperparameter 'mtry' (chosen by maximizing AUROC). I decided to pre-split the training set in 8 samples equals in size ...
0
votes
0answers
33 views

Assessing whether 2 models differ “substantially” (though equal RMSE), to improve prediction

In preparation of stacking several models, I would like to gather some heterogeneous models. I am not sure, but expect that the stacking will improve when I have very different models, though all have ...
0
votes
2answers
150 views

A multi-label classification for tagging short text

I am fairly new in the area of text mining and want to practice my skills a little. I have the following task at hand which I want to work on. I have a large list of short texts (~100.000) and every ...
5
votes
2answers
187 views

Why discriminative models are preferred to generative models for sequence labeling tasks?

I understand that discriminative models, such as CRF(Conditional Random Fields), model conditional probabilities $P(y|x)$, while generative models, such as HMM(Hidden Markov Model), model joint ...
0
votes
2answers
52 views

Predictive algorithm validation

In putting a binary 1/0 predictive algorithm into production, what are the consequences where only the positive (1) predictions are checked, meaning only true or false positives are detected, and then ...
2
votes
1answer
545 views

Hidden Markov model for event prediction

Question: Is the set-up below a sensible implementation of a Hidden Markov model? I have a data set of 108,000 observations (taken over the course of 100 days) and ...
0
votes
0answers
35 views

Looking for a list of modelling techniques for continuous response with missing values in predictors

I am looking for a list of modeling algorithms (as a package in R) that can accept: Continuous or categorical predictors Continuous response Can effectively ...
8
votes
3answers
1k views

Cross-validation or bootstrapping to evaluate classification performance?

What is the most appropriate sampling method to evaluate the performance of a classifier on a particular data set and compare it with other classifiers? Cross-validation seems to be standard practice, ...
2
votes
0answers
42 views

Using sequential observations to perform online prediction

I'm trying to perform predictions from a sequence of events. My problem is this: Data collection: Suppose you can continuously observe a person sitting in a library. You take note of every time that ...
6
votes
1answer
779 views

How to predict new data with spline/smooth regression

Can anyone help give a conceptual explanation to how predictions are made for new data when using smooths /splines for a predictive model? For example, given a model created using ...
2
votes
0answers
49 views

Prediction errors for Adaboost

I understand how the adaboost algorithm works to produce a prediction of a class, however one thing I haven't seen is how to get a measure of accuracy for that prediction. For example, if I fit a ...
2
votes
1answer
94 views

What is the difference between the values in the 'fit' attribute of a gbm object and values computed by gbm.predict?

My intuition is that the fitted values and predicted values of a gbm object should be identical. But in this example with just one tree, the values are different: ...
2
votes
0answers
110 views

Measuring parameter sensitivity and variability (standard-error) in k-fold cross-validation

I mainly use k-fold cross-validation for parameter tuning and model selection for prediction problems. Now, is there a standard or if not a less-known way to measure the sensitivity of the parameters ...
2
votes
2answers
232 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 ...
2
votes
1answer
165 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 ...
6
votes
2answers
348 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 ...
3
votes
2answers
366 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
183 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
196 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 ...
8
votes
2answers
249 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
157 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 ...
1
vote
0answers
53 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 ...
6
votes
1answer
582 views

Predicting football match winner based only on the outcome of previous matches between the two teams

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 ...
4
votes
1answer
193 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 ...
8
votes
5answers
434 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 ...
4
votes
0answers
496 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
130 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
76 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
69 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
244 views

Gaussian process - dimensionality reduction

I saw a method for dimensionality reduction for the squared exponential covariance function (not ARD) whereby one uses a $G\times D$ projection matrix $P$ ($G < D$, $D$ = dimension of the inputs) ...