In this paper, we present two linear mobility models, namely Linear Regression, and Auto-Regression, to predict the temporal behavior, particularly the residence time, of individual users. We run performance evaluation experiments on two different WiFi mobility traces datasets made available through ...
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 ...
mT0 and BLOOMZ), specifically tuned to the nuances of the Spanish language, to increase their efficiency in AE and QG tasks; (2) A meticulous evaluation and comparison of the three multilingual models, focusing on their respective performance when fine...
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...
Saltoglu (2004), “Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets:A Reality Check” Working paper. :Lee,T.H,B.Saltoglu.Evaluating predictive performance of value-at-Risk models in emerging markets:a reality check.. 2001...
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 ...
Hyperparameters can make a big difference in the performance of a machine learning model. Many Kaggle competitions come down to hyperparameter tuning. But after all, it is just another optimization task, albeit a difficult one. With all the smart tuning methods being invented, there is hope tha...
Therefore, a rigorous assessment of prediction performance is performed on various statistical and machine learning techniques in an attempt to determine the ‘best’ predictive model. The modeling techniques include logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE)...
Rank. After evaluation, the networks get ranked based on the achieved performance and simplicity. Comparing two network models with similar performance, the one with the simpler structure gets chosen. 4. Vary. New network topologies are created by mutation of existing simple networks by adding nodes...
We examined activity during the period when they received the prime (professional vs. student), the period when they listened to the music, and the period when they made the decision about which performance they preferred, to understand the time course and mechanism by which contextual information...