One of the enhancing use case of randomness subjected to machine learning is Random Forests. If you are familiar with Decision Tree that is used inasmuch as vast amount of data analysis and machine learning problems, Random Forests is simple to grasp.
For the beginners, decision tree is a simple, deterministic data structure for modelling decision rules for a specific classification problem (Theoretically shortest possible message length in Information jargon). At each node, one feature is selected to make instance separating decision. That is, we select the feature that separates instances to classes with the best possible “purity”. This “purity” is measured by entropy, gini index or information gain. As lowing to the leaves , tree is branching to disperse the different class of instance to different root to leaf paths. Therefore, at the leaves of the tree we are able to classify the items to the classes. Continue Reading