A Comparative Study on the Effectiveness of Various Machine Learning Paradigms in Image Recognition and Predictive Modelling
Deepak Upadhyay, Mridul Gupta, Aditya Verma, Saksham Mittal
Abstract
The efficiency of several machine learning algorithms in picture identification and predictive modelling is thoroughly compared in this research article. The necessity to find the best algorithms for certain use cases and the rising need for reliable and effective image recognition systems across a variety of areas are the driving forces behind the study. In order to assess how well these algorithms, perform in terms of accuracy, precision, and recall, the study uses a wide range of machine learning techniques, including classical and deep learning approaches. The study's findings show that convolutional neural networks, in particular, outperform conventional algorithms in image identification tests while standard and deep learning techniques perform similarly in tasks requiring predictive modelling. The study also emphasises the value of transfer learning and fine-tuning strategies in obtaining cutting-edge performance on picture datasets, and finally we will see results at the end.