Application of Random Forest Model in Predictive Analysis of Big Data

Abstract

The use of predictive analytics in boosting business intelligence and forecasting across a variety of applications is something that both businesses and researchers are very interested in advancing. Big data analysis presents a significant challenge for conventional analysis techniques because data is expanding so quickly every day. One of the most adaptable and user-friendly machine learning algorithms is the Random Forest algorithm. Additionally, it is helpful in analytics that fall into other categories, such as data collection, processing, analysis, and interpretation. The random forest technique works well for obtaining correct data and for identifying missing data. As of late, different measurable methods, including bootstrapping procedures, grouping strategies, and direct relapse models, have been altered to deal with Big Data. They are powerful, nonparametric and measurable approaches that incorporate the concept of recurrence difficulty and aspects of two-class and multiclass characterization in a single flexible construct. Focusing on the characterization challenge, this white paper proposes a specific investigation of current configurations for scaling random forests to big data problems. These ideas rely on online or simultaneous random forest transformations. We likewise go through how these methodologies manage out-of-sack botch. Then, with regards to big data, we propose different remarks for random forests. At long last, utilizing two tremendous datasets — one mimicked and the other genuine data — we test five adaptations.