Random Forests

Regression and classification are just two of the many tasks that may be performed with the reliable machine-learning algorithm Random Forest. It is an ensemble method, which means that a random forest model is composed of numerous little decision trees, known as estimators, each of which generates a separate set of predictions. The estimators' estimates are combined with the random forest model to yield a more precise prediction. Although they perform slightly less well for regression issues, random forests are excellent for classification difficulties. This may be taken into consideration, and the random forest generalizes well to unseen data, including unobserved data with missing values, thanks to its ensemble design. Additionally, random forests excel at managing huge datasets with high dimensionality and diverse feature types.

Learn more Web Scraping terms: