Since the initial standpoint of science, technology and AI, scientists following Blaise Pascal and Von Leibniz ponder about a machine that is intellectually capable as much as humans. Famous writers like Jules Continue Reading
Since the initial standpoint of science, technology and AI, scientists following Blaise Pascal and Von Leibniz ponder about a machine that is intellectually capable as much as humans. Famous writers like Jules Continue Reading
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
For my research I required a Random Forests implementation but could not find any satisfying one. Therefore , I decided to do it my self. Here is the code that I master for a couple of days. It is based on decision tree implementation in C language and interfaced to Matlab. If you use please put on some feedback in Github or here to the below.