Random forest
Classification and regression are a significant part of machine learning. In different businesses, such as predicting if a particular customer will purchase a commodity, or predicting if an appropriate credit will default, such ability to accurately distinguish observations is extremely valuable (Pal, 2005). Data science offers a variety of algorithms such as logistic regression, naive Bayes classification model, and logistic regression. Nevertheless, the random forest classification is near the top of the hierarchy.
As its name suggests, random forest comprises of multiple decision trees that work together. The individual trees record a class prediction in the random forest, and the model prediction becomes the class with the most votes (Pal, 2005). A simple but powerful idea behind a random forest is the intelligence of the crowds. The explanation of why the random forest model performs so well in data science is:
Some uncorrelated (trees) models working as a group outperform each of the other models. The key in a random forest is the low association model. Just as an investment with low correlations shapes an overall portfolio, unrelated models may generate ensemble predictions that are more accurate than any of their projections (Pal, 2005). The fact that the trees protect each other from individual errors that are the wonderful effect is of random forest. While some trees are wrong, numerous other trees are correct, so that they can move in the right direction for a group of trees.
References
Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222.