The Two Classes of Supervised Machine Learning – Machine Learning Fundamental Concepts

The Two Classes of Supervised Machine Learning

• Regression
• Classification

Regression

Basically, regression is a model that was made to find the relationship between the independent variable (features) and the dependent variable (output). In machine learning, it is used for predictive modeling, where an algorithm anticipates a series of continuous outcomes. Its result is a continuous numerical value, like the current temperature, the number of items sold, the price of the item, or the total number of customers who have visited the store during the holiday season.

Regression techniques that are typically used include

• Linear regression
• Decision tree
• Random forest
• Neural networks

Classification

Classification models are the second type of supervised learning technique. Being a model that classifies data into different groups, it can be used on both structured and unstructured data. Predictive modeling classification is the task of approximating the mapping function from discrete input variables to discrete output variables. The main goal is to identify which class or category the new data will fall into. Assign probabilities between zero and one to all classes of data. Primarily, classification algorithms are used to make predictions about two classes and to divide the results of their work into various categories. Classification algorithms could use the content of an email to figure out if it is spam, just as they could use clinical measurements to figure out if a patient is at risk for a heart attack or diabetes.

In machine learning, there are two types of classifications:

• Binary classification: If the problem has only two possible classes, it is called a binary classifier.
• Multiclass classification: If the problem has more than two possible classes, it is a multiclass classifier.

The following are some popular classification algorithms:

• Naïve Bayes
• Logistic regression
• Support vector machine

Unsupervised Machine Learning

This model is all about finding the hidden patterns in unstructured data without any supervision or guidance from labeled data. Unsupervised learning doesn’t use any labeled data to find its direction or to train itself. It sees groups based on their features and characteristics and classifies them into different units. Using machine learning algorithms to analyze and cluster unlabeled datasets, we find hidden patterns in the data without any help or support from any label.

Clustering

It is an unsupervised learning method that involves putting data into different groups based on how similar or different they are. Clustering can also be thought of as grouping the data together. Items with the most similarities are kept together in the same cluster, as opposed to objects from other clusters that have no or very few similarities with them. A clustering model can show the relationship between features without using labels. In this model, the features are grouped together based on their similarities. In the real world, we can use clustering to group shrubs, herbs, and trees based on their unique traits. In the same way, we can use a car’s emissions to classify it as either electric or gas powered.

The following are some popular clustering algorithms:

• Hierarchical clustering
• DBSCAN
• K-means clustering