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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Customer life time value prediciton [on hold]

I'm interested in predicting lifetime value for new and existing customers. Which data mining techniques are common for this? I've thought of using Linear regression or a multiple logistic ...
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22 views

Determining the effect of number of likes

Let's say I have marketing data and I need to determine how effective the marketing is. The marketing strategy is to publish facebook posts at inconsistent intervals. The goal is to see how the ...
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20 views

what is the size of data should be predicted to make the predictive model valid

if I have time series with 1000 values , and I want to build a predictive model , how far in the future should i successfully forecast to make my predictive model valid, is there any condition or rule ...
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12 views

How do I model chapter-verse references?

Context: I am part of an 8-person group in which each person posts a Bible verse every day. For those who don't know, that is of the format "Psalm 30:1" where first we reference the chapter, then the ...
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25 views

Prior predictive density given by $f(y) = {f(y\mid \lambda) g(\lambda)}\big/{g(\lambda | y)}$?

(I guess stats.SE is the right place for this) I'm reading Albert's book "Bayesian computation with R". To get theprior predictive density, he extensively uses this formula $$f(y) = \frac{f(y\mid ...
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20 views

Data augmentation techniques for general datasets?

In many machine learning applications, the so called data augmentation methods have allowed building better models. For example, assume a training set of $100$ images of cats and dogs. By rotating, ...
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21 views

What regression analysis to use? IVs with two levels and a DV with two conditions?

I'm trying to figure out what the best regression test to use for my data. I have three predictor IVs each with two levels. I also have a DV values belonging to two different conditions (A & B). ...
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1answer
28 views

Question about training set and test set

Suppose I need to compare 3 different regression models. Suppose that my only purpose is to select the model which predicts the response variable better. Be M1, M2, M3 these three models. So I ...
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2answers
31 views

Traffic volume/flow prediction method

I have traffic volume data (Surrey City, CA) like this I wish to use Artificial neural network (Deep Learning) or ARIMA to predict traffic flow/volume of the urban area with the use of previous ...
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19 views

CTR Enhancement Model

I am looking to build a model to enhance CTR. Below is the business description. We have a coupon based website. For each retailer, we have a retailer page. Each retailer page will have up-to 100 ...
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16 views

How far can we predict in time series of price index?

If I build a model for time series that represents the price index of a stock market for 5 years, how far can I predict in the future? The reason for this question is that I want to be sure that the ...
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4 views

Evaluate and report fit of a model on validation cohort(s)

I trained a random forest regression model M on a training set. I am interested in how well the model predicts the responses in 3 different validation sets. I am also interested in the characteristics ...
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6 views

Seasonality in Modeling Population

To conquer the effect of seasonality in data, it is recommended to take multiple sample windows, with each having equal performance window. Question - Should we discard seasonality faced sample ...
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19 views

benefices of big data on machine learning methodologies

I know that there are a number of predictive models (generized linear ones, trees, neural network, support vector machines, knn, Naive Bayes, ...) that have been proposed to perform various analytical ...
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40 views

Logistic Regression Model with Non-Independent Regressors

I'm looking to create a model that takes into account multiple logistic variables in an ordered process. To illustrate, what I'm trying to do is similar to the following: ...
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36 views

How to choose between VOMs and Predictive models, e.g., ARIMA?

In time series prediction, there is a lot of work that uses predictive models (e.g., ARIMA). On the other hand, there's also a lot of work that uses Variable Order Markov models (e.g., context ...
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23 views

Distribution of output from accuracy {forecast}?

I'm trying to work out a method for "online" or live model evaluation for models used in forecasting. One approach is to use the R package strucchange, but it ...
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1answer
37 views

Which are the most important predictors (and how great is their impact) of a continuous dependent variable?

I have a continuous outcome (dependent) variable, which is body weight and I'm wondering which of my 20 candidate predictors (independent variables) are the most important ones for prediting body ...
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3answers
39 views

How to use Random Forest for categorical variables with missing value

I have a labelled dataset of 1M rows and 600 features. I am trying to build a supervised learning model for prediction. I am particularly working with Random forests in R.The data I have has following ...
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7 views

Characteristic Analysis : Variable Stability

What is the calculation of "Score Points" in characteristics analysis report (model stability analysis)? Is it the average of predicted probability falling in a particular variable cohort?
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9 views

Treatment for High Population Stability Index

What are the ways we can stabilize population if we have high population stability index greater than 0.2 in a predictive model? Or how to adjust if it is less than 0.2 but greater than 0.1?
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1answer
37 views

Random forest regression prediction for high dimensional data

I am working on a project by using a high dimensional data set. Close to 50000 Obs. with 392 Variable. I used lasso to reduce it to this point from a total of 1200 variables. And the whole data set ...
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2answers
36 views

How is the chance-level confusion matrix calculated?

I applied an ML technique on my dataset, and got this confusion matrix: 0 1 0 162 62 1 27 50 Funnily, the overall accuracy is worse than ...
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30 views

Predictive modelling and cost function

I have to help a company to detect customer in a list of prospects. The company has this benefit/cost function: Value of a new customer = $20 Acquisition cost = $5 So if the model: Miss to ...
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12 views

Using predictive algorithms for descriptive purposes? Correct? Any resources?

Newbie here. I've been learning lots of predictive modeling with R/python using the well-known algorithms: decision trees, random forests, SVM, logistic/linear regression, k-Means, k-NN. But I'm new ...
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1answer
47 views

More Statistical Way to Average N Predictions

I've run a RandomForestRegressor (Scikit Ensemble) over N loops, each time changing the random seed and therefore changing the train test split. This way I've N sets of predictions (M predictions for ...
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10 views

How to perform pattern identification using ML?

I have the following problem: An event, takes place at a determined day of the week, hour, and with a pre-defined format (movie, music concert, lecture (3 items). Based on exit polls we determine 3 ...
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23 views

What will be the simple interpretation for the coefficients for features obtained in any Machine learning models?

I am working with a data that consists of two classes. I have used scikit learn, to craete models using SVM, Randomforest etc.I used to r2_score and I sorted the scores for features I am having and I ...
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1answer
17 views

Missing variable in the external validation cohort

I have developed a model on my training cohort and I intent to use an external dataset to validate the model. However, one of the predictors used in the model was not captured in the external ...
2
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1answer
43 views

Model to predict categorical outcome from continuous and categorical variables

I have to fit a model to test whether Learning (1=learned, 0=failed) depends on lizard sex (M or F), Lizard SVL (snout-vent length), or an interaction of the two. I am new to both R and this website. ...
2
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1answer
14 views

Random effects for a continuous variable in a mixed Effects Models

I intend to fit a mixed effects model and all of my covariates are continuous. One of the covariates, say x2, is time (from enrollment) when the treatment was initiated and it is thought (by ...
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1answer
23 views

scale of variance in Gaussian process

When performing Gaussian process regression, the variance at a prediction point is given by $var[f_*] = k(x_*,x_*) - k_*^T(k+\sigma_n^2I)^{-1}k_*$ (Equation 2.26 from GPML) The variance is not ...
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14 views

Technology Acceptance Models can be used for prediction?

I am planing to conduct a survey to predict the adoption of Mobile Banking service in the country where it was not introduced before. I am wondering whether it is possible to apply the Technology ...
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29 views

Scaling factor in R?

I am implementing a logit model on fraud detection data set which is having several attributes.Target column is truth or fraud.Data contains transaction amount is one of attributes.I generally scale ...
3
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2answers
47 views

Comparing Categorical Variables

What is your go to method to visualize relationships between categorical variables? At work, I find myself working with a discrete outcome variables quite a bit. When exploring data, I often want to ...
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60 views

What methods can I use to aid in modeling a smaller data set when I have a significantly larger data set with fewer variables?

I currently have a data set with about 4,000 rows. The current model I have established for it is not very good, and I am going to receive more data for about 150 of these points, and I'm hoping that ...
4
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1answer
65 views

Evaluate posterior predictive distribution in Bayesian linear regression

I'm confused on how to evaluate the posterior predictive distribution for Bayesian linear regression, past the basic case described here on page 3, and copied below. $$ p(\tilde y \mid y) = \int ...
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8 views

Should information crtieria be applied to training or validation data?

Information criteria for selecting models seem to be applied to training data in general. Could they also be applied to validation data to decide the most predictive and simple model, or is this ...
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1answer
17 views

Hosmer-Lemeshow test with weighted data

I am trying to perform Hosmer-Lemeshow test on weighted data (i.e. each observation in a data set has its weight). Unfortunately, I cannot find any literature on how to perform such test. Do you know ...
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7 views

Best approach for using predictive anlaysis to improve upon survivalist data

I previously have done alot of work on Arrhenius modelling in JMP using Censoring data variables. The factor that causes accelerated failure rates is Temperature and so the activation energy function ...
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1answer
33 views

Predicting value over time

I'm trying to predict the value of a variable after a specified number of days. I'm assuming it will change each day by a normally distributed random amount. For example, today the value is 10. Over ...
2
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1answer
55 views

How to test predictive power of ARIMA model

Once I've fitted an ARIMA model (by choosing, say, the one with the lowest AIC), how can I go about gauging how effective it is at forecasting a given financial time series? Should I somehow ...
2
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30 views

Predicting customer churn

I'm trying to decide how to go about this problem. I have a large database of customers, both who have churned at some point, and who are current. I'm not sure how to create test/train sets from ...
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20 views

Leave one out cross validation error term interpretation

I have a dataset that involved 70 participants and 7 variables (1 y variable and 6 explanatory variable). I have used leave one out cross validation to assess the model and have resulted in an answer ...
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1answer
39 views

Applying randomforest algorithm (fit) on new data without recomputing the fit [closed]

I have a need to do realtime predictions for individual rows of data based on a previously computed randomForest algorithm. How can I run the "predict" command without recomputing "fit" on the entire ...
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37 views

Sample size needed to validate classification/prediction model

Dose any rule of thumb exist (or possible calculation) regarding sample size needed to validate an binary classification model. We have developed this prediction model for a medical condition and ...
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1answer
73 views

Why Binning Variables in Predictive Analytics?

Lot of discussion in CrossValidated focuses on optimal binning methods, binning example etc. But I am trying to figure out what are the scenarios that I have to bin variables whereas it's better idea ...
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16 views

Prediction model on hybrid data

I am currently working with a data set where I have both continuous, discrete and categorical (without any order) data. And I have to predict a continuous data. To be concrete, my problem is a ...
2
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2answers
62 views

Combination Forecast - Which models to pick?

Combination Forecasting can be produced by simply averaging different forecasts or employing more complex techniques (see Makridakis, 1989; De Gooijer and Hyndman, 2006; Goodwin, 2009; Pesaran and ...
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How to merge different predictive models training with different data sets?

Is there any good method to merge/consolidation different predictive models which were trained on different features but outputs the same goal. Example: Model 1 with features a + b + c (trained on ...