Random Forest

The random forest algorithm is one of the most popular and most powerful supervised machine learning algorithm in the modern world. It is capable of doing both regression and classification tasks. It develops lots of decision trees based on random selection of data and random selection of variables. It provides the class of dependent variable based on many trees. As the trees are based on random selection of data as well as variables,these are random tree. Many such random trees leads to a random forest.

Advantages of random forest:
1. Can be used for both classification and the regression task.
2. Random forest classifier will handle the missing values and maintains accuracy for missing data.
3. It won't overfit the model.
4. Can handle large data set with higher dimensionality.
There are some disadvantages as well. Let's look at some of them:
1. It's not as good as for regression as it does not give precise continuous nature predictions.
2. We have very little control on what the model does.

The Applications of Random forest:
1. It is used in the banking sector. It can used for finding the loyal and fraud customers.
2. It can be used in medicine where it is used to identify the correct combination of components to validate medicine. It is also used for identifying disease by analysing the patient's medical records.
3. It is used in the stock market to identify the stock behavior as well as the expected loss or profit by purchasing a particular stock.
4. In e-commerce the random forest is used in a small segment of the recommendation engine for identifying the likelihood of a customer liking their recommended products.
5. In computer vision random forest is used for image classification.