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A More Accurate Approach for Prediction using Gradient Descent
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 10 |
| Year of Publication: 2022 |
| Authors: Shweta Agrawal, Ravishek Kumar Singh |
10.5120/ijca2022922036
|
Shweta Agrawal, Ravishek Kumar Singh . A More Accurate Approach for Prediction using Gradient Descent. International Journal of Computer Applications. 184, 10 ( Apr 2022), 18-22. DOI=10.5120/ijca2022922036
Abstract
Accuracy is one of the most important concerned when we are dealing the problem related to machine learning. Artificial intelligence of also one of the most popular and emerging field which used optimization to improve accuracy. Gradient descent is a new and most power techniques which integrate machine learning and AI to achieve optimization. There are so many techniques are available for optimization and Gradient descent is one of then it is a kind of an iterative algorithm and most used for discovering the minimum cost function. This technique help to take efficiently and successfully decisions, by the use of derivatives. Derivative is useful when we want to calculate slope of the graph from a particular point. The value of the slope is defined by representation a tangent line to the graph at the point. In this technique we have to calculate this tangent line, with help of this we are able to calculate and decide the direction to reach the minima. It is the best techniques for optimization in machine learning. It is based on first-order optimization. This technique used a objective function, in which we have to update parameter in the reverse direction for every iteration. In the paper apply this optimization techniques to find better linear regression line and try to fit as best regression line for given data set. Our objective is to reduce SSE error and improve cost function value.
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