Machine Learning Algorithm Variants

Machine learning algorithms variations are based on how the algorithms learn. Working out the rules and programming them is also a hard task. This is where your technical expert will help. Machine learning algorithms can be classified into three categories:

1.    Supervised Learning.

2.    Unsupervised Learning.

3.    Reinforcement Learning.

1. Supervised Learning

Algorithm is given a set of inputs (known as labelled or tagged data) and then instructed what the expected output is. In supervised algorithms, concepts are taught, data is familiarized to an extent that decisions can be made on new data.

Supervised learning is like performing a task, which you were taught before and you have a fairly good idea about the expected result for the given set of inputs.

Supervised algorithm works out rules that can give expected output for the given inputs. This decision making ability then enables algorithm to forecast new inputs and make decisions. When new data is encountered, existing data and rules are utilized to understand and to make decisions on the data.

Supervised algorithms are expected to forecast on data.

Think Of Supervised As Guided Learning By Human.

Examples: random forests, decision trees etc.

2. Unsupervised Learning

Unsupervised learning algorithms are purposed to model structures, data distributions and workout the results themselves.

Inputs are given without expected outputs.

Unsupervised learning is like performing a task that you have not experienced before and you start the experience by gathering as much information as possible. Imagine learning a language without knowing the basics of the language.

When new data is encountered, data is classified first and then categorized into clusters or groups. Finally, decisions are made on the new data.

Think Of Unsupervised Algorithms As Self-Taught Algorithms.

Classification information is given and then large data is fed into the algorithm so that it can classify the data into appropriate groups and then make decisions.

Unsupervised learning can be used to solve problems that are very complex in nature as the algorithms can learn to solve problem themselves.

Examples: K means, clustering etc.

3. Reinforcement Learning

Inspired by behavior psychology, the algorithms are mainly used in Game theory and simulation optimization methods. Reinforcement learning concept revolves around agents taking actions based on the reward of their previous actions.

Example: SARSA