• Vaibhav Narayan Chunekar
• Dr.Nilkmal More

Machine learning algorithms have revolutionized various fields by enabling computers to learn from data and make predictions without explicit programming. Classification, a cornerstone of predictive modeling, categorizes data into predefined classes based on features, facilitating applications like spam email detection and medical diagnosis. Classification techniques generalize patterns, make predictions on unseen data, and enable automated decision-making. In this paper, we explore the significance of Random Forest and Decision Trees, powerful classification algorithms in supervised learning. Through methodologies and applications, we highlight their predictive performance, interpretability, and ease of implementation, underscoring their role in advancing machine learning.

Machine learning, classification, supervised learning, Random Forest, Decision Trees, predictive modeling, interpretability, algorithm evaluation, Python, sklearn.

"Exploring the Significance of Random Forest and Decision Trees in Supervised Learning for Cancer Dataset: A Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.h288-h292, May-2024, Available :http://www.jetir.org/papers/JETIR2405735.pdf

"Exploring the Significance of Random Forest and Decision Trees in Supervised Learning for Cancer Dataset: A Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pph288-h292, May-2024, Available at : http://www.jetir.org/papers/JETIR2405735.pdf