scholarly journals That takes the BISCUIT: A comparative study of predictive accuracy and parsimony of four statistical learning techniques in personality data, with data missingness conditions

2020 ◽  
Author(s):  
Lorien Grey Elleman ◽  
Sarah K McDougald ◽  
David M Condon ◽  
William Revelle
Keyword(s):  
Random Forest ◽  
Comparative Study ◽  
Statistical Learning ◽  
Big Five ◽  
Regression Models ◽  
Predictive Accuracy ◽  
Complete Data ◽  
Elastic Net ◽  
Learning Techniques ◽  
Statistical Learning Techniques

The predictive accuracy of personality-criterion regression models may be improved with statistical learning (SL) techniques. This study introduced a novel SL technique, BISCUIT (Best Items Scale that is Cross-validated, Unit-weighted, Informative and Transparent). The predictive accuracy and parsimony of BISCUIT was compared with three established SL techniques (the lasso, elastic net, and random forest) and regression using two sets of scales, for five criteria, across five levels of data missingness. BISCUIT’s predictive accuracy was competitive with other SL techniques at higher levels of data missingness. BISCUIT most frequently produced the most parsimonious SL model. The elastic net and lasso dominated other techniques in terms of predictive accuracy with complete data and in conditions with up to 50% data missingness. In terms of predictive accuracy, regression using 27 narrow traits was an intermediate choice. For most criteria and levels of data missingness, regression using the Big Five had the worst predictive accuracy. Overall, loss in predictive accuracy due to data missingness was modest, even at 90% data missingness. Findings suggest that personality researchers should consider incorporating planned data missingness and SL techniques into their designs and analyses.

scholarly journals That Takes the BISCUIT

2020 ◽  
Vol 36 (6) ◽  
pp. 948-958 ◽  
Author(s):  
Lorien G. Elleman ◽  
Sarah K. McDougald ◽  
David M. Condon ◽  
William Revelle
Keyword(s):  
Random Forest ◽  
Statistical Learning ◽  
Big Five ◽  
Regression Models ◽  
Predictive Accuracy ◽  
Complete Data ◽  
Elastic Net

Abstract. The predictive accuracy of personality-criterion regression models may be improved with statistical learning (SL) techniques. This study introduced a novel SL technique, BISCUIT (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent). The predictive accuracy and parsimony of BISCUIT were compared with three established SL techniques (the lasso, elastic net, and random forest) and regression using two sets of scales, for five criteria, across five levels of data missingness. BISCUIT’s predictive accuracy was competitive with other SL techniques at higher levels of data missingness. BISCUIT most frequently produced the most parsimonious SL model. In terms of predictive accuracy, the elastic net and lasso dominated other techniques in the complete data condition and in conditions with up to 50% data missingness. Regression using 27 narrow traits was an intermediate choice for predictive accuracy. For most criteria and levels of data missingness, regression using the Big Five had the worst predictive accuracy. Overall, loss in predictive accuracy due to data missingness was modest, even at 90% data missingness. Findings suggest that personality researchers should consider incorporating planned data missingness and SL techniques into their designs and analyses.

scholarly journals Reply

2005 ◽  
Vol 133 (12) ◽  
pp. 3724-3729 ◽  
Author(s):  
Vladimir M. Krasnopolsky ◽  
Michael S. Fox-Rabinovitz ◽  
Dmitry V. Chalikov
Keyword(s):  
Statistical Learning ◽  
Numerical Models ◽  
Interdisciplinary Field ◽  
Learning Techniques ◽  
Statistical Learning Techniques ◽  
Speed Up ◽  
Network Application ◽  
Similarities And Differences ◽  
Methodological Aspects ◽  
Modern Machine

Abstract This reply is aimed at clarifying and further discussing the methodological aspects of this neural network application for a better understanding of the technique by the journal readership. The similarities and differences of two approaches and their areas of application are discussed. These two approaches outline a new interdisciplinary field based on application of neural networks (and probably other modern machine or statistical learning techniques) to significantly speed up calculations of time-consuming components of atmospheric and oceanic numerical models.

scholarly journals Force Field Parametrization of Metal Ions from Statistical Learning Techniques

2017 ◽  
Vol 14 (1) ◽  
pp. 255-273 ◽  
Author(s):  
Francesco Fracchia ◽  
Gianluca Del Frate ◽  
Giordano Mancini ◽  
Walter Rocchia ◽  
Vincenzo Barone
Keyword(s):  
Force Field ◽  
Metal Ions ◽  
Statistical Learning ◽  
Learning Techniques ◽  
Statistical Learning Techniques ◽  
Force Field Parametrization

scholarly journals Beyond Traditional Measures of Personality with BISCUIT and BARE: A New Statistical Learning Technique and Behavioral Item Pool to Push Personality Psychology Forward

2020 ◽  
Author(s):  
Lorien Grey Elleman
Keyword(s):  
Statistical Learning ◽  
Big Five ◽  
Traditional Approach ◽  
Item Type ◽  
Item Pool ◽  
Pilot Studies ◽  
Personality Psychology ◽  
Learning Technique ◽  
Statistical Learning Techniques ◽  
Behavioral Frequencies

This dissertation investigates two ways in which personality psychology should move beyond the traditional approach of measuring personality with broad domains composed of trait descriptors, as exemplified by the Big Five taxonomy. The first study (Chapter 2) suggests an alternative to the traditional approach of aggregating personality items into domains. Mounting evidence indicates that, compared to domains, narrower measures of personality account for more variance in criteria and describe personality-criterion relationships more accurately. Analysis of individual personality items is the most granular approach to studying personality and is typically performed with statistical learning techniques (SLTs). The first study: (a) champions a new statistical learning technique, BISCUIT; (b) finds that BISCUIT provides a balance between prediction and parsimony; and (c) replicates previous findings that the broadness of the Big Five traits hinder their predictive power.The second study (Chapter 3) suggests an alternative to the traditional approach of measuring personality with trait descriptors, or "traditional personality items." Of the three patterns commonly associated with personality (cognitions, emotions, and behaviors), behaviors are the least studied; traditional personality items tend to measure cognitions and emotions. Historically, yearlong patterns of specific behaviors have been thought of as criteria of personality measures, but the second study posits they should be classified as personality items because they measure patterns of behavior, a component of personality. The second study reviews and extends two pilot studies that indicated behavioral frequencies predict life outcomes, sometimes better than traditional personality items. The second study: (a) estimates the extent to which behavioral frequencies strengthen personality-criterion relationships above traditional personality items; (b) determines that some criteria are differentially predicted by personality item type; and (c) publishes an updated, public-domain item pool of behavioral frequencies: the BARE (Behavioral Acts, Revised and Expanded) Inventory.

scholarly journals Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

2021 ◽  
Vol 309 ◽  
pp. 01163
Author(s):  
K. Anuradha ◽  
Deekshitha Erlapally ◽  
G. Karuna ◽  
V. Srilakshmi ◽  
K. Adilakshmi
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Random Forest ◽  
Regression Models ◽  
Solar Power ◽  
Machine Learning Techniques ◽  
Pv Systems ◽  
Solar Power Generation ◽  
Learning Techniques ◽  
Solar Power Forecasting

Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

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