Advantages and Disadvantages of Random Forest. 
What is the difference between Bagging and Random Forest?. How does it work? (Decision Tree, Random Forest). Also, you'll learn the techniques I've used to improve model accuracy from ~82% to 86%. For ease of understanding, I've kept the explanation simple yet enriching. I've used MLR, data.table packages to implement bagging, and random forest with parameter tuning in R. In this article, I'll explain the complete concept of random forest and bagging. Most often, I've seen people getting confused in bagging and random forest. Its ability to solve-both regression and classification problems along with robustness to correlated features and variable importance plot gives us enough head start to solve various problems. If you are new to machine learning, the random forest algorithm should be on your tips. In fact, the easiest part of machine learning is coding. However, I've seen people using random forest as a black box model i.e., they don't understand what's happening beneath the code. 
With its built-in ensembling capacity, the task of building a decent generalized model (on any dataset) gets much easier. Random Forest is one of the most versatile machine learning algorithms available today. Not for the sake of nature, but for solving problems too!
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