Decision made Easy with Random Forest!!!
When We, Human always want to have a second opinion why not our algorithms?
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Let’s understand decision tree first! It is an algorithm to answer the single question by working through an entire series of queries.
e.g. if we want to predict Temperature, below should be the flow…
- Random decision forests correct for decision trees’ habit of over-fitting to their training set.
- The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees.
So, the IDEA is just many simple ideas combined together to yield extremely accurate models that can ‘learn’ from past data.
By: Antara Basu