Three different regression‐based supervised machine‐learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross‐validation. Strongest performance was observed with support vector regre...
The predictive performance of habitat models developed using logistic regression needs to be evaluated in terms of two components: reliability or calibration (the agreement between predicted probabilities of occurrence and observed proportions of sites occupied), and discrimination capacity (the ability o...
1. However, within this type of procedure one can adopt different strategies regarding training/testing split point, growing or sliding window settings, and eventual update of the models. In order to produce a robust estimate of predictive performance, (Tashman 2000) recommends employing these ...
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecolog... CF Dormann,J Elith,S Bacher,......
(2001). Evaluating predictive performance of value-at- risk models in emerging markets: a reality check. Journal of Forecasting, forthcoming.Bao Y., T. Lee, B. Saltoglu (2006) "Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check ", Journal of ...
models, focusing on their respective performance when fine-tuned on individual (SQuAD, SQAC) and combined Spanish datasets, offering deeper insights into their adaptability and efficacy; (3) An in-depth study of the inherent abilities of the mT0 and BLOOMZ models in a zero-shot setting, ...
Using live match data, these logistic regression models were then used to create phases of play plots of a home team's and away team's performance throughout the progress of a number of games in the 2006/07 season. These scores were smoothed using a Tukey's T4253H smoother to eliminate...
If you have read my post on estimating the performance of regression models without ground truth, the story should sound familiar. There, we have used gradient boosting models to evaluate themselves. The trick was that while the model cannot predict its own error (since that’s the same as ...
Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks. However, there is a current hot debate regarding their reasoning capacity. In this paper, we examine the performance of GPT-3.5 and GPT-4 models, by performing a thorough technic...
Using water quality and weather data collected over four years, several multiple linear regression (MLR)‐based models were developed for near‐real‐time prediction of E. coli concentration and were tested using independent data from the fifth year. Model performance was assessed by the ...