Questions tagged [predictive-models]

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 more emphasis on performance.

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726 views

What is the intuition behind the expected transaction value for a customer in the gamma-gamma model?

Background and Motivation: I was reading the paper RFM and CLV: Using Iso-Value Curves for Customer Base Analysis by Peter S. Fader, Bruce G. S. Hardie and Ka Lok Lee, in an attempt to gain some ...
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360 views

Should I use unpenalized logistic regression, lasso or ridge for explanatory modelling?

When using logistic regression for predictive modelling, the choice between 'standard' logistic regression vs ridge vs LASSO versions of logistic regression seems relatively straightforward - just ...
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566 views

Assessing predictive accuracy on longitudinal data

Suppose you have a longitudinal dataset, in which several subjects, sampled independently of each other, were measured at one or more timepoints each. The timepoints need not have equal intervals ...
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310 views

Reinforcement *Model* Learning

Classical reinforcement learning (Q- or Sarsa-Learning) can be extended with models of the environment. These models are usually transition tables that contain the probability of arriving at a ...
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905 views

Forecasting time-series ahead by multiple time horizons

Suppose that I have daily data on the population of a small village, given by $Y(t)$, as well as daily data on various factors that are relevant to the size of the population in the future, given by ...
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147 views

Including feature-dependent priors on output class, in bayesian logistic regression

When doing logistic regression with data $D_N = \{(x_i, y_i)\}_i^N$ with $x_i \in \mathbf{X}^N$ (each data point has N features) and $y_i \in \mathbf{Y}$ being assigned output classes, in a Bayesian ...
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1answer
381 views

Expected squared distance from origin of training points vs. test points

This is from Exercise 2.4 (Page 39) of Elements of Statistical Learning: The edge effect problem discussed on page 23 is not peculiar to uniform sampling from bounded domains. Consider inputs drawn ...
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1answer
196 views

How to train a model when instead of a target we have a range where it is?

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
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How to combine noisy and noise-free datasets to train a model

Overview Suppose I have two datasets, both of which consist of rows of features and their matching labels. One of these datasets is noise-free and its labels correspond to the ground truth, but the ...
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613 views

Predictive modeling using GAM (mgcv)

I have seven years of continuous insect population data, along with temperature and humidity parameters. I’d like to use this data to predict future populations in a given year using a generalized ...
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204 views

How to evaluate uncertainty estimates in regression?

Some regression algorithms (e.g. Gaussian process regression) can produce uncertainties along with point predictions at test time. These should also be evaluated. How about calculating the Pearson ...
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513 views

Predicting response propensity in a rolling data collection

I'm working with a survey that uses a rolling data collection format (i.e., there are multiple waves of sampling and initial contacts). I'm trying to develop a model to predict how likely a sample ...
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124 views

Graphical nominal model

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

Predictive model (binary) doesn't seem to fit my own data

I have tried to create a predictive model based on the probit model (common in my field). The model is given as: $$\operatorname{Prob} = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{t}\exp\left(-\frac{x^2}{2}...
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1answer
305 views

How to predict routes using clustering data

I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
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81 views

Relationship between Total Over/Under scores and actual total scores in sports

I have a data set of actual scores from sporting games, matched with the bookmaker's Total Over/Under Score (O/U Score) and the odds the bookmaker was offering that the game's total score would fall ...
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1answer
663 views

GAMs vs GLMs with feature engineering - is there a practical difference?

I recently came across this tutorial on General Additive Models (GAMs). Quoting the article: The principle behind GAMs is similar to that of regression, except that instead of summing effects of ...
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1answer
475 views

Modeling delayed feedback using logistic regression

Suppose we are trying to model the probability of a user clicking on an ad using logistic regression. We will receive only the positive feedback so, we define $Y = 1$ when success was observed, $Y=0$ ...
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1answer
437 views

How can I show that the posterior predictive distribution for GPs is a gaussian?

I am given a set of training values $X$ and $y$, and the goal is to predict the value $f_* \equiv f(x_*)$ for a new data point $x_*$, where $f(x) = x^Tw$. How can I show that: $$ \begin{align} p(f_* | ...
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185 views

Choice of classification loss function with unequal payoffs

Suppose that I'm building a binary classifier parameterized by $\theta \in \mathbb{R}^k$ that maps some observed features $x_i \in \mathbb{R}^l$ to a decision of whether or not to play a game with an ...
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1k views

Gaussian process regression and optimizing an RBF kernel for forecasting?

I'm using gaussian process regression with an RBF kernel to forecast a time series. I'm using GaussianProcessRegression in ...
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1k views

How do I make use of “soft” labels in binary classification?

Let's say we have a binary classification task, but our dataset contains more fine grained values of how much an examples belongs to the class or not. So the labels are real numbers in $\left[0,1\...
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220 views

How to retrain a production classifier that blocks its own positive examples?

I'm looking for help understanding how to re-train a fraud detection classifier that's been deployed to production (where it successfully blocked much, but not all fraud coming into the system). I ...
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188 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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131 views

R HoltWinters - Odd dip in predictions

So, I have this time series that tracks the daily number of applications to a graduate program. Each application period is 64 days - so for each period, you start at zero and it goes up until the ...
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1k views

ARIMAX for modelling daily sales

I am trying to model daily sales for a take out restaurant. They are only open on business days - no holidays or weekends - as their primary clients are office workers on their lunch breaks. Below is ...
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115 views

What analyses do I need to run?

Long post. Question at the bottom. My data look likes this: It consists of the test set accuracies of a series of 50 models formed on training sets. Using random sampling of the raw observations, ...
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76 views

Predicting user selections based on similar user

Lets say you give a set of users a set of polls, or give them a choice of foods to eat, or let them listen to a group of songs (guess like pandora). So looking at the choices that all the users make ...
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338 views

Autocorrelated predictors in linear models

I need to predict the outcomes of a time-series variable $Y$ based on two time-series predictors $X1$ and $X2$. For simplicity I will only illustrate $X1$ in the rest of this question. The ...
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1answer
441 views

Correct cross-validation procedure for single model applied to panel data

Questions What is the correct CV procedure for panel data? I've been thinking of the problem as cross-validating a model fit to multiple time series data. Is the "population informed" CV procedure ...
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1answer
105 views

Generalized least squares error estimation

First of all, I have to admit that I am not statistician so some of my nomenclature could not be very rigorous and maybe a bit confusing; pleas ask me to clarify if necessary. The Problem Let's say ...
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Should I use statistical tests (e.g., Hosmer-Lemeshow) to assess predictive models?

Generally, is it useful to carry out statistical calibration tests on purely predictive models? For instance, if I build predictive model and I choose final model relying on cross validation results (...
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Reducing a logistic model used for prediction

I'm developing a logistic regression used for prediction. I have pre-selected, based on prev. literature, 15 candidate predictors (fitting my ~200 events). Now, I want a reduced/more parsimonious ...
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2answers
84 views

How to handle machine learning inputs that should be considered as set of vectors, but whoes interpretation is order invariant (order agnostic set)

Basically wondering best practices for input modeling and ML algorithm type(s) for inputs that essentially model samples that are a bag/set of "sub-objects", so order does not matter. Think of the ...
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Modeling true number of sales based on reported number of sales

I have a sequence of numbers of quarterly sales spanning a decade, say $\{Z_i; 1\leq i \leq m\}$. These numbers are not observed until much later. I do have incomplete observations of this sequence ...
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145 views

Assessing correlated predictions

Let's assume we have a prediction algorithm (if it helps, imagine it's using some boosted tree method) that does daily predictions for whether some event will happen to a unit (e.g. a machine that ...
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372 views

Interaction effect in random forest

I'm interested in interaction effect between variables in random forest. I found some information here https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#workings. The operating ...
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1answer
123 views

Common variable transformations

I want to predict a variable $Y$ given a set of variables $X_i$. To account for nonlinearity, my $X_i$ are put in several quantile dummies, so that I prefer transforming my $y$. My $Y$ variable are ...
3
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1answer
39 views

Using clusters to estimate model variance

I am working with a blackbox prediction model which takes known inputs and outputs a single mean response. I know this model's residuals to be heteroskedastic, but also can assume the error term of ...
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93 views

Why are the predictions of my models getting worse?

I am creating a series of monthly models to predict the yearly production of grain for a fixed US state. For a given month, the model is built using the data from the past 10 years and it allows me to ...
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124 views

Random Forest Models: creating correlated features

I'm trying to understand how correlated (multicollinear) predictors affect predictive power and / or variable importance in tree models, e.g. Random Forest models. Particularly, I'd like to know if ...
3
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1answer
65 views

In real clinical diagnostic data set how can we know the “true label” of a patient?

When we were taught about Bayesian probability, we often saw the following example: in a population, there are 5% of people who has disease X, and among the people who have disease X, the current ...
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770 views

Tobit model (or survival analysis) for imputation of censored data in R

I have some data with missing values. For the missing observations I know a range, in which the true values are. I want to use a tobit model to predict the variable with these missing values. The ...
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98 views

model selection using cross validation

I was wondering about model selection problem. To be more specific, how to split the data and use cross validation. So let's imagine situation: We want to create some predictive model on data set D. ...
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542 views

Neural networks and signal-to-noise ratio

My guess is that neural networks do not work very well in noisy environments, i.e. the lower the signal-to-noise ratio, the worse the result of a neural network, if compared to other statistical ...
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650 views

Accounting for autocorrelation in predictive models?

Is it necessary to account for autocorrelation of data in a predictive model? I ask because it seems that accounting for autocorrelation (temporal or spatial) by means of ACF, PCF, etc. is typically ...
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2answers
4k views

which predictive model should I use if column is having string values?

I want to create a predictive model to predict a categorical value (1,0) but the independent variables are also having few string columns (country, location, age) which I feel are important for the ...
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204 views

Predictability of predictor variables in regression analysis

I have run a multiple linear regression analysis to predict the forecast of demand (in litres) of soft drinks. I have 104 sets of weekly data and my independent variables are feature space (measured ...
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0answers
307 views

Using a time series as covariate in regression model

I have a binary outcome variable (Disease/No disease). In a diagnostic test for this disease, 20 different sensors record a time series value. These time series are relatively correlated, but each ...
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351 views

Multiple time series - class of problem with agents and events?

I'm working on a prediction problem and struggling to find applicable resources (articles, tutorials, papers) that address this class of problem. I'm assuming the info is out there and I'd love to ...

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