Random Forest Algorithm Random forest is one of the most widely used supervised machine learning algorithms, and as I said, this is used both for classification and regression. Random forests are built on the concept of decision trees, and that's where I was saying that it's good if you know decision trees already. Decision trees are one of the simplest algorithms in terms of implementation as well as in terms of explaining how things are working behind the scenes, right? So, decision trees work like a flowchart. You start from the root node where you give all your data, the training dataset, and based on a lot of different parameters like entropy, Gini index, or other algorithms, it kind of divides your dataset into at least two subsets, as shown here. The data is here, the main dataset, now it gets divided. So, suppose this is a classification problem. Now we are saying the data gets divided into two subsets: one is this, and one is this. Right? So, this is subset A, and this is subset B. How does this data split happen? It happens on different algorithms, which I am talking about, like Gini index, entropy, variance reduction, or other algorithms. The primary idea is that we are trying to segment the data so that it gets purer. By "purer," I mean the final objective is that all the data should belong to one class. When I have the data on the first node, and I have just two classes—false, true, and false—of course, I have data mixed for both classes. Now, I'll run some mathematical functions, and I will try to divide this data so that I have more purity or lesser impurity in a node. This means, if there were 50 rows of my data, out of which, say 25 were false, and 25 were true, this is a completely impure node. I would try to bring some data where I have a larger false data set here, and maybe, you know, a larger true data set here. I keep on doing this until I get either completely pure nodes, or I have some threshold set. So, that's a very high-level way of understanding how decision trees work. Now, as I said, these are the simplest form of algorithms because it's very easy to explain to a business user what is happening, unlike other models which you might have seen, like regression, neural networks, or any other model where some mathematics is going on behind the scenes, which is difficult to explain to business users. But here, it's very simple to implement and explain. If that is the case, if decision trees are so helpful and so easy to use, then why do we need to do something on top of decision trees? So that's what we have to look at: why do we need random forest? One disadvantage of decision trees is that they do not perform very well on real datasets; they tend to overfit. Now, overfitting is a concept where the model does very well on your training dataset but does not perform well on your real data or test data, which means you have very high variance. Decision trees generally have low bias, which means they fit very well on your training dataset, but they have very high variance, meaning they don't do well with different datasets that they haven't seen. To overcome this problem, we still use decision trees, but in a very different form, which is called random forest.