Popular Machine Learning Methods
Supervised Learning and Unsupervised Learning are two of the most widely used machine learning methods. However, there are other methods as well and in this article we are going to discuss about some other popular machine learning methods.
Supervised Learning: As the name suggests, there is some sort of supervision involved in this set of machine learning algorithms. Here, the algorithm is trained using labeled data. For example, consider a manufacturing process where each component from the machine is labeled as defective or non-defective. So along with the label assigned to each component, we also capture the process parameters (information regarding all machine/manufacturing parameters while that component was being manufactured). This way we have a set of input features (machine/manufacturing parameters) as well as an output variable (the label). Now a supervised learning algorithm will take this set of information (both the input and the output), learns by comparing its actual output with correct outputs to find errors, modify the model accordingly, and identify the relationship between each input feature and the output variable. The relationship can then be used to predict the output value for a given set of input values.
Supervised learning is primarily used to solve problems where historical data predicts likely future scenario. For example, it can be used to identify which product is defective or which customer is likely to respond to a marketing promotional campaign.
Unsupervised Learning: As the name suggests, there is no supervision involved in this set of machine learning algorithms. Here, the algorithm is not told about what to do (the label is not marked), instead, the algorithm must figure out what is there in the data. The goal of unsupervised learning is to explore the data to find some hidden pattern or structure in the data. Unsupervised learning tends to perform better on transactional data. For example, basis the behavioral attributes of the customers, it can create segments which can be used to design appropriate marketing campaigns specific to each of the segments. Some common unsupervised machine learning algorithms include nearest-neighbor mapping, k-means clustering, and singular decomposition.
Semisupervised Learning: As the name suggests, semi-supervised Learning uses best of both the worlds (Supervised and Unsupervised). In this method, the algorithm uses both the labeled and unlabeled data for training the model. Generally, it uses a small amount of labeled data and a large amount of unlabeled data. This approach is primarily useful when the cost of labeling is high (unlabeled data is less expensive and takes less effort to acquire). It can be used to solve classification, regression and prediction problems. Facial recognition is one such common application where semi-supervised learning is used.
Reinforcement Learning: This machine learning approach is used primarily for navigation applications, robotics, and gaming. Here, the algorithm learns using trial and error method to yield the greatest rewards. The simple way to understand the working of reinforcement learning algorithms is to understand the three underlying components:
- Agent (the learner or decision maker),
- Environment (everything the agent interacts with), and
- Actions (what the agent can do).
The objective in reinforcement learning is for the agent to choose actions that maximize the expected reward over a given amount of time.