Tagged Questions

Predictive models are statistical models whose primary purpose is to predict other observations of a system optimally, as opposed to models whose purpose is to test a particular hypothesis or explain a phenomenon mechanistically. As such, predictive models place less emphasis on interpretability and ...

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2
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2answers
58 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
0answers
42 views

How does predictive model for the Eurovision Song Contest work?

I've encountered interesting prediction of Eurovision Song Contest http://mewo2.com/nerdery/2013/05/12/eurovision-2013-first-predictions/ it based on some kind of Bayesian model I assume but I don't ...
2
votes
2answers
45 views

Advice needed on auction system simulation

I am trying to simulate an auction system in which a number of competitors, $N$, independently offer a discount from a reference price previously published by the buyer. The order is awarded to the ...
0
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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 ...
0
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0answers
22 views

Should the number of parameters depend on the purpose of the model?

I am curious what are some arguments for/against increasing/decreasing the number of parameters depending on the purpose on the model. I am currently building a model which will be used for ...
0
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0answers
11 views

Mean absolute percentage error (MAPE) in Scikit-learn

How can we calculate the Mean absolute percentage error (MAPE) of our predictions using Python and scikit-learn? From the docs, we have only these 4 metric functions for Regressions: ...
0
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0answers
52 views

Statistical tests on the revenue data of a small business

I have daily revenue data from a small business with 6 locations. The business sells food products that range from roughly \$2.00 to \$9.00, mainly to professionals. They do over a million dollars a ...
3
votes
1answer
41 views

How do I combine two predictors?

I am trying to classify a data set with 2 boolean values. I have two classifiers that may/may not be independent. The first one is 65% accurate, and the second one is 60% accurate. Can I combine ...
0
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0answers
28 views

Question about predictive bias - intercept and slope bias

I am slightly confused on how to determine a slope and intercept bias. I have an assignment where i am supposed to conduct a gender predictive validity bias analysis. However, my lab handout and the ...
5
votes
2answers
77 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
63 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
122 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
19 views

Predict binary occupancy vector from history of vectors

I have a set of binary vectors where each vector represents one day of occupancy in a house and consists of 48 elements (each element for 30 minutes of the day). Each element can be 1 meaning that ...
2
votes
1answer
36 views

How to report the results of cross-validation for comparing two models?

I want to compare the predictive power of two models. For this, I calculated the difference in some measure of predictive performance over many cross-validation replications. Now I have a distribution ...
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 ...
0
votes
1answer
46 views

Algorithms for regression analysis which can handle large scale datasets

I am a CS undergraduate student and for my final project i developed a regression algorithm that is suited for large-scale datasets (i wouldn't say 'Big Data', but still large scale). For the final ...
0
votes
1answer
76 views

AIC, BIC, DIC, model selection criteria

I am trying to understand the difference between these parameters, and their application. Was hoping to get some correction/clarification to my statements. I have a training set and cross-validation ...
0
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0answers
36 views

Pre to Post Repeated measures for categorical dependent variable and more than one independent variables both continues and catgorical

I am doing Prenatal (before delivery) and Postnatal (after delivery) depression assessment (2 time points). Dependent variable is Depressed/Non depressed mother on a measure of depression. When as ...
0
votes
2answers
47 views

In SVM, what are the labels and how do you get them from the data?

I'm working on a school project and have decided to use SVM for stock market prediction. I have a 1000x5 matrix of stock quotes containg data for open, close, high, low, volume data. From what I ...
1
vote
0answers
19 views

Hierachical Predictors in a Regression

Note: Mainly this question pertains to predictions from a model. If the unit of analysis of a regression (or any predictive model really) is the individual retail store and these stores are organized ...
1
vote
0answers
18 views

Calculating error bars for Excel Linear Regression [duplicate]

I've ben sent a forecast of sales from a consultancy. It uses Excel's LINEST function, taking 4 factors that seem to have affected sales in the past, and used them to make a prediction. How do I go ...
6
votes
2answers
99 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 ...
2
votes
0answers
34 views

Modelling rainfall to size storage tanks

I have daily rainfall data for a given site going back about 30 years. I have a building with an average daily demand for water of $L$ litres and a catchment of $A$ m$^2$ with a runoff coefficient of ...
2
votes
1answer
46 views

Re-scaling a confusion matrix after down sampling one class

Let's say I have a large, un-balanced binary classification problem (in reality nrow is more like 500k, and ncol is more like 500): ...
0
votes
1answer
42 views

Model assumptions not met but model has predictive capabilities

This is a general question, has probably been asked before (I searched and did not see similar), may have a simple answer, may be ridiculous, and is just to satisfy my own curiosity. Say we build a ...
4
votes
2answers
70 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
29 views

Various models not improving basic rpart result

I have a data set with 10,000 or so samples in it and 100 or so features. I've created a training set and test set and am trying to predict a numeric value. I've used rpart to determine the most ...
0
votes
0answers
40 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 ...
2
votes
2answers
82 views

Medical Insurance Fraud Detection: Text analysis

I'm trying to analyse a dataset to detect fraudulent insurance claims. Unfortunately, other than basic demographics the rest of the claim is a free format OCR scanned text file made from documents ...
3
votes
0answers
37 views

Modeling pass rates for departments and courses within a school

Suppose I have a regression model, for example a logistic regression model, which provides a score between 0 and 1 reflecting whether or not that a student will pass a course given certain variables: ...
0
votes
0answers
15 views

Can Kalman filter apply to distribution function?

The standard Kalman filter uses a series of measurements observed over time, to decomposite the signal and noise. However, when I'm modeling the distribution (pdf or cdf) of a variant, is there a ...
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 ...
1
vote
1answer
74 views

What is behind JAGS (Just Another Gibbs Sampler)?

I have been using JAGS but I am not quite sure how it actually simulates it values. I need to know in a general sense what's going on in the background. Thanks for the help
3
votes
2answers
98 views

How I can deal with too many variables in training a data set?

I am trying to train a predictive model on whether a given person is ( male or female) based on behavior cues we've obtained from online surveys. The dependant variable will be a binary ( 1 or 0 ...
0
votes
1answer
45 views

Simulated single value based on multiple chains in RJAGS

I am using RJAGS to simulate the posterior distribution of event that a certain candidate will win the presidential election. I need to find the actual percentage that one of the candidates will have. ...
2
votes
1answer
138 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 ...
4
votes
0answers
98 views

Model performance in quantile modelling

I am using quantile regression (for example via gbm or quantreg in R) - not focusing on the median but instead an upper quantile ...
0
votes
0answers
27 views

learn a decision tree classifier with uneven training data

Hi i have a set of data from which I wish to learn a decision tree classifier(binary) using id3. There is a 70:30 split between the classes should I expect the classifier to preform better on the ...
0
votes
2answers
157 views

What statistics should I use for evaluating the accuracy of predictions?

I have two variables representing 1) players' predicted fantasy football points and 2) players' actual fantasy football points scored. What statistics are best for assessing the accuracy of the ...
0
votes
0answers
72 views

Compute categorical variable importance for logistic regression

I am dealing with huge(2 lac rows = 200,000 rows) dataset with a combination of categorical and numerical variables for predicting binary values. My data set format looks like : ...
0
votes
0answers
12 views

Terminology for seperately modelling the conditional prediction error of a model?

I have a modelling task in which knowing the accuracy of each prediction is at least as important as the prediction itself. I also know that the accuracy is highly variable; there are certain ...
3
votes
1answer
50 views

Mixing User Data For Cross-Validation

I have data from 12 users, and want to perform cross-validation. Is it necessary that I create my training and test data from different users, or can all of the data be randomized, and then split into ...
1
vote
0answers
45 views

Assessment of a single predictive model

I am facing a situation where I need to assess the predictive ability of a single (regression) model. I do not need to compare its performance with some other model, I just need to show that it ...
2
votes
1answer
87 views

Predict 2 responses from two co-variates

I'm not quite sure how I should fit a model that has two responses. The data consists of target (x,y) co-ordinates and actual (x,y) co-ordinates. I would like to fit a model to predict a new set of ...
1
vote
1answer
98 views

How to validate and compare models predicting a binary variable?

I have a question about determining which models are "better" and how to assess that info. Let's say I have three models, each which predicts our bid on won ping. Our bid is a continuous variable and ...
7
votes
5answers
251 views

Classification vs. regression for prediction of the sign of a continuous response variable

Say I want to predict whether or not a project will be profitable. In my sample data, the response variable is actually a continuous variable: the $ profit/loss of the project. Because my ultimate ...
1
vote
2answers
57 views

Is it possible to get high AUC while the correlation between predictor and response is very low, around 0.01?

My data has three continuous predictors and one binary response. I built a logistic regression model but AUC is only 0.52..it's almost like the model did nothing.. Then I calculated the correlation ...
4
votes
1answer
60 views

Predictive performance depends more on expertise of data analyst than on method?

I've come across a rumour that some study showed that the performance of predictive models depends more on the expertise of the data analyst with the chosen method than on the choice of the method. In ...
0
votes
0answers
23 views

Prediction with a subset of features

Given only a subset of features on which my model was trained, I need to find how likely different values of a categorical variable are. For example, I have data with about 20 000 observations of 100 ...
3
votes
1answer
216 views

Imputation with Random Forests

I have two questions on using random forest (specifically randomForest in R) for missing value imputation (in the predictor space). 1) How does the imputation algorithm work - specifically how and ...

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