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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Dependent predictors with converse effects on the target

I am trying to create a predictive model for marketing in the natural gas field. The model is supposed to guess how probable it is to make a contract in that particular building given many internal ...
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26 views

Suggestions needed about classifier fusion

I'm working on a classification problem which involves two classifier to observe a single event. I'm providing a high level description of the problem without going into the technical details (the ...
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1answer
18 views

Modeling remaining duration for prediction

Suppose we're in the business of repairing broken specialty widgets and reselling them. At each point in time, we want to predict how much cash we'll make in the next 30 days on the existing ...
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47 views

Linear Regression In R and test of constant variance

I am trying to construct a regression model in R.I am getting an error while predicting the model. I am not sure if the newdata(which is my validation set) should be a data frame? ...
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11 views

Algorithm questions [closed]

Hello I am Trying to build an multiple choice Examination system Through the use PHP/javaScript/Html Where in ITEM analysis is Present. Item In which DescribeL: How many got correct in this number ...
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17 views

How to develop a robust procedure to select a predictive model

Imagine you have a matrix, M, of n input variables and m values per variable. There's also a ...
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68 views

Predictive posterior distribution with multivariate normal distribution

Suppose I have a multivariate normal ${\bf{Y}}|{\bf{\theta}} \sim {\bf{MVN}}(X {\bf{\beta}}, \sigma^{2}H(\phi))$ where ${\bf{Y}}$ is a set of observations ${\bf{Y}} = ...
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glmer predictions: how to extract scores that contributed

I've fit a logistic regression mixed effects model with glmer in R and I'm doing predictions with it. Given a new data that needs a probability prediction, I am interested in extracting the fixed and ...
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68 views

Importance of multiple linear regression assumptions when building predictive regression models

As far as I know, one can differentiate between two main goals of the regression analysis: The goal is understanding causal relations between variables. Here, one has to check several common ...
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163 views

Should train and test datasets have similar variance?

If variance of test dataset is lower than the one of the train dataset is it worth splitting the data? Since we know our dataset will always be limited is it fair to select models under the above ...
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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 ...
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22 views

Initiator follower analysis with time series data sets

I am a newbie to this forum. I searched different white papers and codes on google but couldn't find a solution, that's when I registered on this forum.. Please share in case you guys have a idea as ...
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12 views

Multiple regression with dependent variables

I have a dataset with 3 variables (X,Y and Z) and I want to find the best estimates for the constants a,b,c & d. I have been looking into multiregression analysis, but that does not seem to work ...
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22 views

How to retrieve the prediction equation in R?

I have developed a prediction model prototype in R. The model uses Support Vector Regression to predict. But I need to develop the entire solution in Visual C++ for a real life implementation. I ...
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1answer
34 views

Residual Value Prediction For Used Electronic Products

I am trying to predict the long term residual value of a product with only the releasing price. I have collected some data off the Internet related with one phone type, and it is pretty obvious that ...
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42 views

R - Linear Regression - Control for a variable [migrated]

I have a computer science background & I am trying to teach myself data science by solving the problems available on the internet I have a smallish data set which has 3 variables - race, gender ...
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2answers
43 views

What other predictive models should I consider

I have a numeric dataset involving 34 input variables and one output variable. I'd like to fit a model to the data so to be able to make predictions (regression problem). So far I've tested ...
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19 views

How to model when actionable data is generated on a daily basis

Trying to build a predictive model for attrition prediction of service desk agents using logistic regression. Data available: Daily performance metrics such as call quality,avg. call ...
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61 views

Product price prediction - include important external factors

I need some hint over what is the general prediction solution to modelling products prices in such a case: I have several models (types) of the product I want to predict prices for each of these ...
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179 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) ...
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3answers
152 views

How to choose data for training a predictive model for attrition prediction

Trying to build a predictive model for attrition prediction at service desk/call center. Have daily data on the following parameters: 1.Call quality - QTM (0-100%), 2.No. of calls - Calls(Number) ...
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29 views

Classification tips for a begginer

I'm doing a graduation work that involves applying Classification algorithms in a dataset of matches from Dota 2 (a popular MOBA game). Here's an explanation of the problem: Dota 2 matches are played ...
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14 views

How to compare fit of discrete process with discrete underlying process?

I am basically looking for an equivalent to something like an $\mathbb{R}^2$ for a model on a dataset that is itself simply a collection of points. That is, if my data set is (trivial example): ...
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19 views

How to evaluate a trained model using parameters other than AUC in RapidMiner?

I am using RapidMiner to build predictive models trained and cross-validated by a set of medical data(65 cases. 18 attributes), I am now running trials by trying different combinations of learners and ...
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50 views

Prediction vs. Explanation and its Effect on Statistical Methods [duplicate]

In layman's terms, what is the difference between predicting and explaining in statistics? I was looking for the differences between AIC and BIC and found this post with an answer stating: My ...
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39 views

Call Centre Models

Can anyone suggest me, which Mathematical(Statistical) methods can be used to predict incomingcalls by a given time interval at a call centre? Please cite any ...
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34 views

Understanding the Rank Probability Score

The ranked probability score (RPS) is a measure of how good forecasts that are expressed as probability distributions are in matching observed outcomes. Both the location and spread of the forecast ...
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52 views

Removing the intercept term in a dynamic regression justified?

I'm trying to build a cross-sectional prediction model (dynamic panel) with the following form: (including a LDV) $Y_{i,t+1}=a+bY_{i,t}+cX_{i,t}+e_{i,t+1}$ As the sample contains for example ...
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128 views

How to form a predictive model in R?

I have two data sets whose structure is like this: DATA SET 1: ...
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60 views

How to develop a prediction model based on correlation in R?

I have two sets of data, say sales and profit, and I have calculated the correlation between these two data over different months using R. So currently I have ...
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39 views

Evaluating predictive models

I am looking for ways of evaluating the performance/success of predictive (classification) models for economic purposes. I know of: Direct accuracy percentage AUC score Net profit Rate of return ...
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8 views

Different sufficiency condition for Goods and bads

I am building an underwriting model for a bank with the following construct Development Sample Window - Accounts opened before Jun'13 (i.e. have completed atleast 12 months as of Jun'14) Bad ...
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29 views

Predictive Model from Counts Data

I have some data that is the number of times a person visited a doctors office over a course of $5$ years. I want to create a model that would be able to predict the most likely number of counts that ...
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1answer
58 views

Cross-validation with dummy variables?

Does it make sense to use cross-validation with factor variables that have 3+ levels? When using bestglm, I get an error saying that it doesn't work with categorical variables. In the documentation ...
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1answer
290 views

Predicting the Weather

Given a tree trunk with concentric circles, can we predict the weather for each year? Each concentric circle accounts for a year that the tree has been on the Earth. The innermost circle is the oldest ...
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21 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 ...
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29 views

Is this poor transformation advice for predictive modeling?

I have gotten some advice from a PhD statistician on doing predictive modeling on large datasets (lots of variables AND lots of observations) that I should perform transformations to eliminate ...
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67 views

Forecast with predictable market events

I'm trying to build a predictive model to forecast the residual value of used electronic equipment. As a first step, I created a few quick plots in order to visually identify any interesting features. ...
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38 views

How to preform non-parametric bayesian based regression (predictions) in R?

I am working on some non-parametric bayesian based predictive analysis using R. I have a set of data which denotes various parameters of an online transaction. Based on these parameters I want to ...
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10 views

Values in predictors have same pattern for two possible value in dependent variable?

I am trying to build a model for auto rejection of crowd-sourced task (eg: human name transcription of census data). My data set has 5 predictors, and dependent variable is either correct or ...
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1answer
50 views

How to avoid random forest overfitting and improve prediction?

I have an input dataset x_train and an output dataset y_train ...
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37 views

How does Vowpal Wabbit handle NA's or missing values?

I'm working on a problem that involves a large amount of NA's. How does VW work around this? Should I try to impute the NAs with colmeans or something similar before piping into VW format?
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40 views

Concordance vs. Sensitivity

I am confused between these two terms: sensitivity and concordance. What I understand about these two terms: Concordance: the number of pairs where actual 1s have higher predicted probability of ...
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1answer
83 views

Difference between prediction in R and SQL

I'm working on a prediction model for a continuous variable (amount of medicine injected) .I use R for modeling.My project flow is to multiply the prediction of a glm (logistic regression) model that ...
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24 views

Modeling Counts With Small Observations

I am new to Cross Validated SE so I am going to try and formulate my question to the best of my ability. I have a large data set that contains $5$ different fields. The fields are ...
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1answer
26 views

Model specification with Deflators: methodological question on forecast model

I am trying to build a model to predict one year ahead Earnings per share $(t+1)$ based on variables in year $t$. I’ve seen a lot of models in practice that use the following methodology: ...
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36 views

Out-of-time testing (basic question)

I understand the importance of out-of-sample testing, but could you tell me why I should (or shouldn't) do out-of-time testing ? The only use that comes to mind is if the predictive model applies to ...
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1answer
43 views

Using OLS for Model Selection and Prediction - Heteroscedasticity Issue

I am new to regression and having problem in solving Heteroscedasticity in OLS. Have done lots of homework and test before seeking your advice. Sharing the background and what I have done to solve the ...
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31 views

Predictive modeling techniques for in-sample rather than out-of-sample prediction?

Is it appropriate to apply predictive modeling variable selection and shrinkage techniques (for example, ridge regression or lasso) for in-sample prediction rather than out-of-sample prediction? ...