Data: Linear Regression Modelling of Population and GDP using Adaptive Learning Rate Optimization Algorithms

Published: 16-03-2021| Version 1 | DOI: 10.17632/5g2grgyw82.1
Contributors:
Stephen Okonkwo,

Description

Adaptive Learning Rate Optimization Algorithms (ALROA) are used in solving machine and deep learning problems. This study aims to adopt these algorithms to estimate the coefficients of traditional linear regression models between global population and GDP by countries for the Year 2019. Algorithms such as Stochastic Gradient Descent (SGD), Stochastic Gradient Descent with Momentum (SGD with Momentum), Nesterov Accelerated Gradient Descent (NAG), Modified Root Mean Squared Propagation (Modified RMSprop), and Modified Adaptive Moment Estimation (Modified Adam) were used in this study. A learning rate of 0.01 and exponential decay rates of 0.9 and 0.999 were used for the first and second momentum. Half Mean Square Error (HMSE) was used as the loss function while Root Mean Square Error (RMSE) was used to rank the algorithms in order of accuracy. Keywords: Cost Function, Modified Adam, Gradient Descent, RMSE

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