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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7 views

On the prediction with glmm with unknown random effect

I’m using glmm with logit link and a random intercept to take into account any differences between the years of sampling to model spatial species distribution. At this point, when I compare the ...
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29 views

Difference between SE and 95% CI?

I have results using the SE of the estimated marginal means calculated from the linear mixed model (lmer in R). I was told to use the within-subject 95% CI instead. To what extent does it matter if I ...
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The neural network returns a straight line [duplicate]

I decided to write a neural network to calculate the evolutionary track. I have: MIST tables with evolutionary tracks Dataset converter that converts tables to ...
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Can I use the minimum lambda.1se among 100 iterations of 10-fold cv.glmnet to tune the final model?

I am running 10-fold cross-validation(cv.glmnet) 100 times on a training dataset in R. The purpose of this process is to find the optimal lambda to tune the final model for my prediction model. I did ...
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In a regression problem, having a variable highly correlated with our target messes up the optimization of the parameters?

In a discussion with a colleague, she told me that if a variable X_i in our design matrix (X) is highly correlated with our variable of interest (target, y, etc), it will make the regression unsovable ...
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23 views

Probability distribution of new data given old data

Say we observe data $D$, which comes from a probability distribution $P[D|\theta]$, where $\theta$ are the unknown model parameters. Given this information, what is the probability distribution of the ...
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Discussion model selection. Predictive value for daily sales forecast

I am trying to predict the number of sales for a given day for a stand-up comedy cafe. Aside from a variety of predictive values (day of the week, average sales last 30 & 60 days, day of the year ...
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23 views

Re-calculating the errors/residuals in regression using another variable range (Stock price and volatility) [closed]

We are working on using regression to do analysis of stock returns (i.e. predicting the stock price). We have some fundamental metrics like PE ratio and EBITDA (which are the independent variables), ...
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Model Explanation

I have a problem where I am trying to predict something from historic tabular data. We have features {X1,X2,..Xn} and I have a prediction {Y} I am trying to find a way to explain why the model came up ...
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49 views

What use is a test set in a continuous training setting?

I have tuned and trained a model that predicts an aspect of customer behavior from behavior within the most recent 12 months. After initial training, generalization performance was estimated based on ...
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How to interpret this dataset: Symbol Detection for Molecular Communication

I am going to participate to my first data competition, and I have lot of doubts, that I hope you can help me to clarify. This is the background: Detecting the transmitted symbols is challenging in ...
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Are time-series approaches like (V)ARIMA(X) appropriate for making predictions on *new* series?

Say we have a bunch of entities that develop over a time-series in n-steps in a fashion that we hope is similar but just attenuated by some other entity-specific features. The data structure is the ...
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Predicting stationarity of a time series

I have a time series, for example equity prices. This series is weakly stationary (for instance an AR process that does not violate the stability condition). How can I make predictions on how likely ...
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Alternatives of permutation test to proof that a model performs better than chance level

I built a model based on a very high dimensional data, which requires 4-5 days to built using High Performance Computing (HPC). I want to do a statistical test to proof that the predictive power of my ...
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Given a dependent variable how can I determine the relative effect of several independent variables to its value?

Say I have a series of N observations, and for each observation, I have 4 variables: $x$, $y$, $z$, and $q$, where $q$'s value depends on at least two of the other ...
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For a linear prediction model, does selective sampling to encompass the range of independent variable minimize number of samples required?

Suppose we have high confidence that $y=mx+c$ is a good model for a physical process based on previous experiments, and $m$ and $c$ vary with local/temporal conditions. We wish to predict $y$ for new ...
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Adjustment for size of treatment difference

I routinely need to carry out tests for what may be best described as 'treatment recommendation protocols'. In the simplest case I have a model that 'predicts' optimal treatment based on some set of ...
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Prediction of lme object does not show all the range in predictor variable?

I am trying to plot the fitting line associated to a fixed effect in an lme object, using the base package. For that, I am using (this dataset) And this code: ...
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26 views

What does a “flat region” of precision recall curve imply?

I am evaluating ML models (GBDTs) on various test sets using Precision-recall curve, and my goal is: within some precision range, get as high recall as possible. The precision-recall curves on most of ...
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Advice on how to analyze effectiveness of two categorizations of the same data set

I have a set of samples that were categorized based on two different sets criteria. (Low vs High), and (Mild, Moderate, Severe). These categories are supposed to predict the likelihood of becoming ...
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Forecast running challenge completion date in excel

I want to add in a cell that shows the predicted completion date of a running challenge I am doing, based on my performance so far. At the moment I have a cell that works this out on a very basic ...
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Should I make the problem “easier” or “harder” for predictive maintenance by changing the available data?

Consider the standard method of labeling discrete windows "normal, 0" and "failure soon, 1": If in the second case I want to predict further ahead of time, I simply push the 1s ...
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What is the maximum Target cardinality in multi-label classification?

I have a dataset that consists of a target column with 65 classes. Also, the dataset has 200 columns/features. I researched multi-label classification and found the popular algorithms that can be used ...
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How do I determine p and q from an acf and pacf plot?

I know you determine p from and q from pacf and acf, but how do you find the optimal number? I still don't quite get how it's done after googling. What is the optimal p and q from the following charts?...
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What is “categorical target variable” in SAS EM

"A categorical target variable that has exactly two categories (i.e., a binary or dichotomous variable)." What is "categorical target variable" in the SAS EM space? Is this ...
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25 views

how is the expectation of the empirical error based on an i.i.d. sample S is equal to the generalization error?

I am on the 28 page of the book called "foundations of machine learning" by M.Mohri which states that for a fixed hypothesis h,the expected value of the empirical error is equal to ...
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24 views

Using candlesticks for Stock price direction prediction [closed]

I am working on a college project wherein I want my model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as compared to ...
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35 views

Determine family in GAMs of negative values

I have these values (negative and positive) and I want to determine the nonlinear relationship between variable and predictor using generalized additive models (GAMs). ...
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78 views

What is the MSE of a model when the true data generating process is known?

Say I fit an OLS model to get an estimate of the coefficients $\hat{\beta}$. For some reason, I also happen to know with complete certainty: That the predictors come from a multivariate normal with ...
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14 views

How to model the probability of detecting an image, given it is seen multiple times

Are there any existing methods/models describing the probability of an object being detected by a computer vision algorithm given it is seen $n$ times at similar angles and orientations? I know that ...
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23 views

Rates or Per unit quantities as response variables in regressions?

I have a set of predictor variables (X1, X2...XN) and a single continuous response variable (Y) that I would like to build a prediction model from. The Y in this case, is the Volume of material but ...
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72 views

LightGBM model improvement when the focus is on probability prediction

I am building a binary classifier using LightGBM. The goal is not to predict the outcome as such, but rather to predict the probability of the target even. To be more specific, it's more about ranking ...
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Out of sample testing and tail function

Below you see an out of sample rolling window estimation I found here: https://www.r-bloggers.com/2017/11/formal-ways-to-compare-forecasting-models-rolling-windows/ Here is my question: I know the ...
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Not all independent variables available for same time period..how to handle such situations for ML models?

I have 9/10 independent variables for which information is available for last 2 years (including dependent variable). For 1/10 variables the information is only available for last 2 months as this ...
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Forecasting based on previous data values and repeating that each week in R

I have this file: a.csv For example on a.csv, for store 1000 and store 1001, there are values for all 7 days of the week for 3rdparty, dinein, drivethru, digital, and takeout: store day of week ...
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How does classical statistical methods fit into the machine learning paradigm [duplicate]

To elaborate more on the question and give a few examples: classical statistical methods: Frequentist linear regression that involves estimating beta, t-test, p-values, R-squared, F-test. Model ...
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what's the difference between hypothesis function outputed by algorithm A and polynomial function in the book,“foundations of machine learning”

I am on the 28 page of the book called "foundations of machine learning" by M.Mohri which contains this(in the iamge below) defination of the PAC learning framework.I am a bit coufused about ...
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50 views

How to tell a model will do well

My point is how to tell, without testing on new data, a model will predict well new data based solely on training data. Suppose the training data is of very good quality and the selected, final cross-...
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Outlier detection during non linear regression coupled with unkown model selection - how to pick the best model?

When performing non-linear correlation, I have been using AIC to perform a preliminary selection of what models could be a potential good fit for my correlations. I was toying around with the idea of ...
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Could bollinger bands or keltner channels be used to estimate the top of a corona virus wave?

In technical analysis you can use bollinger bands or keltner channels to get an idea of how overbought or oversold the market is. Whenever the line gets above the upper bollinger band then a trend ...
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11 views

Approaches to geospatial temporal modelling of real estate price

Suppose we have a collection of GPS points representing various properties with factors such as the number of bedrooms or area, as well as price that is changing in time. The objective is to model the ...
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18 views

adding noise to prediction task

Say that a teacher wishes to use a standard prediction task from Kaggle as a course assignment, and the idea is to have students submit their predictions, and award grades based on a test set (...
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76 views

What is a partial chi-square statsitic according to Frank Harrell?

In his RMS course (section 4.1.1), Frank Harrell mentions the use of a partial chi square statistic for measuring the strength of association between a predictor and an outcome. See below for a ...
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How to best explain prediction vs uncertainty in the case of probabilities

In my mind, (I think) I understand the difference between prediction and uncertainty about that prediction. The prediction comes from a model (say a LPM or a Probit) and the uncertainty is related to ...
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How are t+n predictions generated?

I'm trying to understand how ARIMA and other models generate or produce a prediction sequence. I understand how they fit the model with each point with the training set, and then testing with the ...
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Mathematical bias and weight vs machine learning bias and weight

I am a little confused about the term Bias and Weight with respect to machine learning. Say we want to predict the heights of people whose weights are given. So plot weights to x-axis and height to ...
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Has there been done work on the accumulation of error of RNN's for predictive modelling?

Lets say I am using an RNN to predict future value $x_{n+1}$. This will be based on a known dataset $\{x_i \}_{i\in[1,n]}$. I assume that the expected error will be higher for $x_{n+2}$ since $x_{n+1}$...
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Internal validation of BART using bootstrap resampling

I developed a BART model for binary outcome predictions given two numeric independent variables using the pbart R package (v2.9). The code looks like this (sub-setting for 23 observations, complete ...
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1answer
45 views

Using GAM effectively with characters vectors

Perhaps I require a simplifcation of GAM models regarding character vectors, although, I cannot seem to use s() to smooth these vectors, it usually returns an error ...
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1answer
36 views

Is it good practice to replace negative predictions with zero?

Given a model that predicts the value of a non negative variable is it okay to replace the negative predictions with zero? Is there a different methodology of dealing with this problem? Are the any ...

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