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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Applications of Dynamic Time Warping (Time Series)

Recently, I came across this algorithm called "Dynamic Time Warp" (e.g. https://cran.r-project.org/web/packages/dtw/vignettes/dtw.pdf). Although this algorithm looks quite involved and ...
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Real World Applications of Simulation

I have been reading about "Markov Simulations and Markov Decision Process" and am trying to understand how these concepts can be used in "regular and everyday life". I see these ...
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Why is my BLUP not Best?

I want to verify, computationally, the bestness of the BLUP for predicting random effects. Let $$ Z=\begin{bmatrix} 1&0&0\\ 1&0&0\\ 1&0&0\\ 0&1&0\\ 0&1&0\\ 0&...
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How to include unobserved variable in a statistical model

I trying to fit a model meant to estimate the change in a variable (say Xm) between two time points t0 and t1, t0 and t1 are the baseline and endpoint respectively. Scenario At t0 we conducted a ...
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Which predictive model to use for distributions like this

I have a target variable that has the following distribution. I have tried the typical regression models such as logistic regression, ridge regression, catboost regression etc. but I'm thinking that I ...
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How to handle highly skewed percentage data

I am trying to make a prediction model (explanation would be nice but mainly predicting as accurately as possible is more important, for now). My independent variables are all percentages (quantity of ...
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Time Series vs. Queueing Models

Generally speaking, queues are modelled using the Poisson Process. Supposedly, this used to model the dynamic nature of queues, arrivals, birth-death and renewals. But just as a basic question: Why ...
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Markov Chains for Hospital Stays

Suppose I have the following problem: Suppose you access to the hospital records: you have the history about how different patients passed through the different "stages" of the hospital (...
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Can probability distributions be used as an alternative for regression models?

Suppose you have 3 variables: height, weight and salary. Can you first attempt to fit a 3 dimensional probability distribution to this data - then, if someone gives you a height and weight measurement,...
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Random Forest Parameter Settings for Big Data

I have a big data set (with more than 9,000,000 rows) with 7 features and 1 label. The label is ordinal data. I would like to run a random forest regression. I'm fairly new to random forests so I have ...
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Probabilities from ecdf and normalised data - is this acceptable difference? (using R)

I have a dataset of 500 observations, definitely not normally distributed: The minimum is around 50 000 in the data. If I use ecdf to predict e.g. 100 000, I get: ...
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Different Methods for Forecasting

Data - Monthly Rainfall of a region for the past 20 years Objective - To Forecast for the next 2 years I am new to time series forecasting and I am looking for suggestions on various methods that I ...
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Predictive or Error Tests for Vector Autoregressive Models (VAR)

I have two questions relating to VAR and would kindly appreciate any assistance/opinion: Question 1: I am having difficulty finding a proper predictive ability test for my VAR model to conclude if my ...
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Help with Predictve Statistcal Tests [duplicate]

so I'm not great with stats but I'm a little confused about this; I've performed a multivariate cox regression analysis using various categorical variables regarding overall survival. My variable of ...
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Normal Linear Model: Prediction of original variable from log transformed variable? [duplicate]

Suppose we have observations $(x_1,y_1),\dots, (x_n,y_n)$ which for some reason cannot be modelled reasonably using a Normal Linear Model. Assume we instead model the log transformed response ...
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Searching for an authoritative answer about the implications of holding random effects to zero in predictions of mixed effects models

Let's say a large population was sampled, and data to construct a model to predict Y were gathered at part of the sample units. To account for correlation among individuals from the same site, ...
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How to interpret the results of the DCA curve?

I have a large sample of data, but only a small number of people have an event. I want to use a certain indicator to predict the occurrence of the event, but when I draw the DCA curve, I found a ...
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Predict gamlss one-inflated beta model

How do you obtain predicted probabilities for the one-inflated component (nu model) of a one-inflated beta regression in gamlss? I have built the following model <...
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Is "probability calibration" intended to improve the performance of a statistical model?

I was watching this video over here: https://www.youtube.com/watch?v=AunotauS5yI This video brought up an interesting point that I never knew had a specific term for (i.e. probability calibration). If ...
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Test subset data with extra variable against predicted value from top model built using full dataset

I am trying to test if a response variable is predicted by spatial/temporal variables + genetics, but I only have genetics data for 25% of the data. Can I test if genetics play a role in the following ...
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Predicting user ratings based on ratings and demographic data on the fly

would like to predict the rating (0-5 stars) a user would give to an item. My data looks like this: for every user I have the age-group, gender-group, and two other factors right after the user ...
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How to build a predictive model to predict Temperature values using voltage values for generated for 1 second

I have a dataset which has voltage values for 1 second sampled each millisecond and number of rows = 200 X = [T1, T2, T3,... T1000] ( each in milli Volt) Y = Target (in Celsius) ----id------| T1(mV)|...
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Individual Survival Curves [duplicate]

Does anyone know if Survival Analysis is being used to make predictions on the individual level? For example, has anyone ever come across any instances where researchers used survival models to make ...
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Which predictive model is best fit here?

I have two predictor variable both numeric with right skew. My outcome variable is binary as positive and negative. Sample size id 157 and positive cases are only 10. That's just 6.37%. I know there ...
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How do I convert my t/p value into a prediction or something meaningful?

I am working with categorical data which has been converted into numerical. Essentially it is antibiotic resistances to certain diseases. I really don't know how best to approach it; how can I use a t/...
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Should one use the usual splitting (Learning/Validation/Test) when using cross-validation?

Say you want to tune several parameters of your model using $N$ data. What you usually do is splitting your $N$ data into 3 sets: learning set: used to build your model; validation set: used to ...
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What are some deep learning models use in timeseries forecasting that include context from covariates?

I was going through the literature for time-series forecasting using DL and all the methods I read about only use the variable of interest at previous timesteps to predict the same variable at time ...
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what does it mean when keras+tensorflow predicts all the timesteps of the Y dependent variable in an ANN?

From what I understand is that in supervised learning problems there is a dependent variable Y, which I included in my ANN. There is one set of matching predictions for each sample for each Y. The ...
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Shrinkage regression (Elastic net) produces worse fit on same data when providing more variables compared to previous fit?

I am currently fitting elastic net models on 45 data points (I know, it's not much) in the context of time series analysis. For context, I want to build a forecast model that forecasts 3 months into ...
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Model for predicting disease that is highly correlated with age

I am trying to train a model for predicting a disease that is correlated with age and gender. Extracted features seem to be both discriminatory for age, gender and the disease. What is a proper ...
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What is the best algorithm to model credit default score for gamification?

Goal: I have a situation that I want to create a model to predict credit default that could handle any missing data for any feature, although my observations don't have any missing data for any ...
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Evaluation metrics for out-of-fold predictions obtained through negative binomial regression

I'm formulating count data out-of-fold predictions using a negative binomial regression and i am a bit confused as to which evaluation metrics apply best. My dependent variable has a long tail ...
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Classification using two text columns and categorical column

I have an audit data which contains 4 columns that I want to apply the classification on. The columns are type of observation(Categorical), Observation(Text), Type of Recommendation(Categorical) and ...
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IsolationForest understanding of contamination, fit and predict

I have a question regarding the parameters within sklearn IsolationForest. I want to train the Model only on normal images. In the next step I would like to check whether a test image is normal or ...
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Strongly and weakly correlated variables and PCA analysis in a prediction problem?

I am doing a prediction assignment as part of a machine learning course using loans data. I have just done some exploratory data analysis on my dataset of just over 9000 rows. There are 11 variables ...
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Is the logistic regression with quadratic and interractions terms (special case) similar to QDA? And what about the prediction performance?

Beyond the fact that the the two methods have different assumptions : Logistic on the residuals extrem value distribution & utility theory. QDA on the predictors multivariate gaussian ...
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Next event prediction - approach

I have a problem that I do not know how to solve reasonably. I need predict date and amount of next (future) order of product. So my data looks like this: ...
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Can I create a model when my dependent variable is always a yes or a 1?

I have a dataset of tropical cyclone formation locations and geospatial data of environmental conditions (parameters) in the area where each storm formed. Can I use this data to create a model of the ...
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Why is AUC so often use to compare performance of different models in churn prediction task?

I have to build model to predict churn and when reading related work on the internet I have realized that in most of the cases the AUC is used as a metric to compare different models. That's ...
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What is Better for Prediction Error: Covariance Penalties or a Test Set?

I'm reading Computer Age Statistical Inference by Efron and Hastie, two statisticians I have a lot of respect for. Section 12.3 discusses Mallows' $C_{p}$, Akaike's information criteria (AIC), and ...
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When is the knowledge of the causal mechanism useful for pure prediction?

In many settings, we are only interested in building a good predictor: e.g. $E(y_t | x_{t-1})$, where $y_t$ and $x_{t-1}$ are vectors of arbitrary dimension. However, sometimes we are also given, or ...
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Forecasting Future Values of Time Series [closed]

I am working with the R programming language. In particular, I am using "Markov Switching Models" for the purpose modelling more complicated dataset with varying degrees of volatility. For ...
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How do I combine predictions of four Poisson regressions that use the same independent variable?

Question: I have a large area, $N_{total}$, where I decomposed spatially into smaller $i$ squares (northwest, northeast, southwest, and southeast quadrants) and ran Poisson regression on these four ...
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In-sample forecast accuracy of Beta (Kalman filter)

One can calculate time-varying betas (known from the CAPM) using the Kalman filter. For example, one can calculate the in-sample forecast accuracy using the MAE. $MAE = \frac{1}{T}\sum_{t=1}^T|\hat{R}...
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R prediction interval - two different methods get two different values

Does anyone know what I'm doing wrong here? I'm trying to get a prediction interval for a linear model using the mtcars dataset. I try two different methods and get two different answers. I'm all ...
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Appropriate analysis method to 'predict' diagnosis of person based on baseline clinical/demographic features

I have data from a cross-sectional study looking at five groups of patients with different diagnoses, presenting heterogeneously to one primary care service. These diagnoses have been confirmed using ...
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Are there methods for quantifying certainty in a non-logistic regression model? [closed]

I've been looking into certainty and how to calibrate classification models such that their predicted probability correlates well with their actual accuracy. I was wondering if such methods existed ...
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Modelling probability of continuous dependent variable from multivariate continuous independent variables

I am trying to create a model that predicts the probability of the size of a continuous dependent variable based on a number of (4) continuous independent variables. My dataset has 67 data ...
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Bad controls, Probit, and prediction

I have three related questions: For causal inference, does a variable that is an outcome of the variable of interest also need to be confounded with the outcome variable for it to be a bad control? ...
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How do I analyse my confusion matrix?

How do I analyze my confusion matrix outcome in terms of FP/TP/FN/TN, precision, etc? ...

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