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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What analysis should be used for a predictive model with multiple discrete responses and categorical predictors?

This is my first question here so I hope I am asking correctly. I have done a few weeks worth of searching and haven't been able to find much, however I'm not a statistician so I probably wouldn't ...
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Can retraining predictive model solves Dataset shift?

Assuming we are using non-parametric models like gradient boosted tree, can retraining the model solves each of the dataset shift (1. covariate 2. prior probability 3. concept shift)?
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Why does the posterior predictive distribution involve an integral?

Given the posterior predictive distribution for a new data point $x^*$, the posterior predictive distribtion given some data $(X,Y)$ \begin{align*} p(y^*|x^*,X,Y) = \int p(y^*|x^*,\omega) p(\omega|X,...
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Using Random forest and keeping sample independence when having multiple observations for each sample

I have a real estate property dataset with the property attributes as independent variables and the asking price at which the property was valuated by a real estate agent as a dependent variable. For ...
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Simulate data based on linear regression and R squared

I have a small data set of 10 x,y points from which I can derive a simple linear regression. I'm looking to use this data set as a basis to simulate / predict additional "y" points as I have a much ...
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LIME Shows Very High Probability Score, But Breakdown Has All Negative Factors

I'm using LIME to break down the observation for each row and am taking a look at the positive and negative factors that contribute to the probability outputted. I filtered my dataset down to only ...
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Two ML models use different features. Does knowing the features of one model help improve the accuracy of the other model?

Suppose two firms are operating in the same field (e.g. insurance). If firm 1 knows which features firm 2 is using in their model, can firm 1 improve its model using that information? What if firm 1 ...
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LASSO/ Elastic Net without test set

I have a small data set (N = 200, 9 predictors, 1 continuous outcome variable) with a lot of noise. I am not able to get "more" data. I want to achieve variable selection. If I split up the data set ...
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Estimate sales of product from sales of related products

The sales of product I'm interested in published every quarter. The sales of related products published every month. How the sales of the interesting product could be estimated from the sales of ...
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decomposing and estimating multiplicative time series

I'm looking through some time series books and looking at different time of models with seasonality we have $X_t = m_t + S_t + Y_t$ or $X_t = m_t*S_t + Y_t$ where $m_t$ is a systematic trend ...
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Is it possible to calcuale CLV at an early stage for individuals?

I'm working on a project in which we have to predict the Customer Lifetime Value (CLV) for a group of customers. In order to calculate CLV in a non-contractual setting, we use probabilistic ...
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posterior predictive distribution of new observations in Bayesian linear regression

I'm confused on how to sample from the posterior predictive distribution of $\tilde{y}$. In class, we've seen that in Bayesian linear regression, the posterior predictive distribution of $\tilde{y}$ ...
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Mean average error normalized by the standard deviation of the target

I'm working on a regression model, aiming at prediction age from structured data. I'm using the mean average error (MAE) as evaluation metric, and want to compare my performances with state-of-the-art ...
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Nested Cross Validation: How to do the whole Shebang (Algorithmic Selection, Model Selection, Parameter Tuning, Preprocessing) [closed]

First post! If you don't want to read the background you can skip to the Problem heading below. Background Hello everyone, I'm a Physics student doing physics education research. My professor wants ...
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Interaction effect when both variables negative

I am trying to create a regression model. Through experience with the subject matter I think there should be a effect on y when 2 of my variables x1 and x2 are both largely negative. x1 and x2 have ...
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Statistical test to compare predictive measures with count data

I'm reviewing a paper that compares predictors of healthcare demand in terms of hospital visits for several conditions. The authors have used a Pearson's rho to determine which is best, and further ...
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Linear regression or logistic regression or other for my problem?

I am working on a physics problem, where I am unsure of the best/most appropriate statistical method to apply. I have some limited working understanding of statistics. The physics problem I am ...
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Which method to use to have precise estimation?

I have Y variable and X variable. R2 is 0,002, when I add seasonal dummy R2 increased to 0,01. However it is the only 2-3 variables X to add. Which method of regression can I use to predict more ...
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Artificial Neural Network and PCA

I have to make a forecast about one variable and I use some different methods to do so. I would try a nonlinear alternative also and I would to consider the Artificial Neural Network (ANN) models. I ...
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Why should I use a calibration curve? If the curve for my model doesn't look right, is my model useless?

I've been scouring the web for more information on calibration curves. Scikit-learn has probably the best documentation I've found thus far. Here's their description: When performing classification ...
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Early paper on equivalence of models, loss functions, and regularizers

I'm trying to find an early paper discussing how models, loss functions, and regularizers can all be seen as the same thing. For example, instead of changing the loss function, I can use the standard ...
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Comparing the predictive power of two non-independent ordinal independent variables

I am looking to compare the predictive power of two ordinal independent variables (RA & 0.5RA+0.5RM) on success (please see image). These independent variables are not independent of each other.(...
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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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How to evaluate multiple time series forcasting model?

Hi I have multiple time series forcasting model and I want to evaluate the predictive power of this model. Let's say, we are predicting $A_T$ and $B_T$ by using $A_t,t\in[0,...,T-1]$ and $B_t,t\in[0,.....
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Testing for clairvoyance (or performance of a model) where the predictions are intervals

I wish to devise a test that determines whether or not an individual is clairvoyant (or if a black-box model works). Let us assume that the clairvoyant believes that they can estimate a person's ...
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For what specific models is multicolinearity a problem in the context of making predictions

For what predictive models (regression and classification) does multicollinearity cause a problem? The reason I ask is the following quote that I read today: Multicollinearity makes it hard to ...
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How can I predict a continuous outcome variable with 1 binary and 1 ordinal predictor variable nonparametrically?

I have a binary predictor variable (situation1, situation2) and another one that's ordinally scaled (levels in an n-back task, they're called no-back,0-back,1-back & 2-back with 2-back being the ...
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Training a model to recognize difference between simulations

I have been facing a problem where I have two simulations. One quite heavy, but potent and able to generalize well the real scenario, and the other which has a low fidelity. Both simulation frameworks ...
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What analysis to use for ABA study design with repeated measures

I have a data set of ~50 subjects including demographic data, such as age, gender, employment, education, and the presence or absence of a health condition. Study Design looks like the following: ...
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Normalise different thresholds for binary prediction

I'm working in a module that outputs the risk of an event happening i.e. risk of a crime happening depending on the district of the city. What I've done is to calculate for each district a binary ...
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Including interaction term without main term with possible aliasing

I'd like to model an interaction term between a continuous variable and categorical variable, while accounting for possible aliasing in the variables. I was wondering what the best way to do this was. ...
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Predicting outputs for new samples in Bayesian linear regression

This is my first question on this forum. I just got started with Bayesian statistics. While I do understand the motivations behind Bayesian methods, I am a little unclear on what the predictions even ...
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Difference Between Sensitivity Rate & Hit Rate

I've been told by my professor that hit rate is more important when it comes to real-life deployment but sensitivity and other accuracy rates need to be satisfactory also. However, my other professor ...
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improving link prediction model on knowledge graph so that it does well on anti-symmetric relation type facts

I was trying to look at the task of link prediciton on the WNR11 dataset. I looked at the TuckER model and found that the TuckER model can do very well on the facts involving symmetric relation types. ...
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Q: AUC of each subgroup is smaller than overall AUC

I have a validation data set of 29242 patients, with known labels/health outcomes and predictions that were generated by some model. 28626 patients are negative and 616 are positive The overall AUC is ...
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Correlation between classes within categorical feature in a ML model

I have a dataset containing numerical and categorical features. Let's say I have one categorical feature C with classes c_0 ... c_n. I build a model that has some ...
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Can you have a concordance statistic (discrimination) when predicting a continuous outcome?

I'm writing a proposal for a prediction model predicting BP (continuous outcome, predicting trend over time). For assessing model performance, I'm seeing discrimination and calibration as the most ...
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What type of algorithm should I use to analyse questionnaires answers?

Let's say I have a questionnaire, part of it are multiple-choice answers. The person answering may answer (a), (b) or (c) for the first question, then (a), (b), (c), (d), (e) or (f) for the second ...
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Combine a probability from a prediction model and the disease prevalence?

I created a prediction model using a multiple logistic regression on a non-representative sample of the target population. With this model, I can get a probability for a new patient of belonging to ...
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Event-based time-series analysis

I'm trying to find patterns in a game event that happens randomly over time. The data looks like this: ...
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What is the best way to present the following predictive regression relationship?

If I have a predictive regression with a single regressor of the form \begin{equation} y_t=\beta x_{t-1}+\varepsilon_t \end{equation} where \begin{equation} x_t=\rho x_{t-1}+u_t \end{equation} Then I ...
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How do I interpret mean absolute error (MAE) or mean absolute percentage error (MAPE) in layman words?

For example, I am predicting a score that can have value from 0 to 100. Lets assume MAPE = 10...
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How do I interpret RMSE in layman words? [duplicate]

For example, I am predicting a score that can have value from 0 to 100. The RMSE = 10. How ...
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How do I approach this problem?

Let's say I have a dataset with multiple types of multiple ingredients (salt1,salt2, etc). Each n-th variation of each ingredient vs flavor may be represented by an n×k matrix that where an ingredient ...
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Is it ok to keep a very strong predictor and other weak predictors in the model? The model built is GBM

Age is coming out as a really strong predictor compared to other variables. This is a classification problem, the dependent variable is a (0/1)
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How can I compare the predictive power or association of two variables of different nature?

I am dealing with the following problem: We have 3 variables: A continuous variable (0 to 1), that is a scoring for people. A discrete variable, offered by a partner, in the range 1..10. That is ...
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How to evaluate prediction error from population?

I have data of 30 subjects. For each subject, measurements have been performed every 5 minutes. Due to the different length of the duration of the procedures, each subject has different amount of ...
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Using Cross Validation for Goodness of Fit for Competing Inferential Models

I have a project where I need to 1) perform inference: understand the role of predictors on the response through models (with my data I need to choose a model where I can reasonably assess how changes ...
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Hypothesis not rejected, what does that mean for expected value? [duplicate]

So I have a linear regression analysis where the confidence interval includes 0, therefore the null hypothesis that the intercept is 0 cannot be rejected. Does this mean that the intercept value ...

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