In this blog, we will describe supervised learning in more detail, and enlist several popular supervised learning algorithms.
Introduction of Supervised Learning
Supervised machine learning is one of the most commonly used and successful types of machine learning. Remember that supervised learning is used whenever we want to predict a certain outcome from a given input, and we have examples of input-output pairs. We build a machine learning model from these input-output pairs, which comprise our training set. Our goal is to make accurate predictions to new, never-before seen data.
There are two major types of supervised machine learning algorithms :
In classification the goal is to predict a class label, which is choice from predefined list of possibilities. Classification is sometimes separated into binary classification, which is the special case of distinguishing between exactly two classes. You can think of binary classification as trying to answer a “yes” or “no” question, and multi-class classification which is classification between more than two classes.
Classifying emails into either spam or not spam is an example of a binary classification problem. In this binary classification task, the yes or no question being asked would be “Is this email spam?”.
In binary classification we often speak of one class being the positive class and the other class being the negative class. Here, positive don’t represent benefit or value, but rather what the object of study is. So when looking for spam, “positive”
could mean the spam class. Which of the two classes is called positive is often a subjective manner, and specific to the domain.
Example of a multi-class classification problem is predicting what language a website is in from the text on the website. The classes here would be a predefined list of possible languages.
For regression tasks, the goal is to predict a continuous number, or a floating point number in programming terms. Predicting a person’s annual income from their education, their age and where they live, are regression tasks. When predicting income, the predicted value is an amount, and can be any number in a given range. Another example of a regression task is predicting the yield of a corn farm, given attributes such as previous yields, weather and number of employees working on the farm.
An easy way to distinguish between classification and regression tasks is to ask whether there is some kind of ordering or continuity in the output. If there is an ordering, or a continuity between possible outcomes, then the problem is a regression problem.
Supervised Machine Learning Algorithms :
There are several machine learning algorithms for supervised learning:
Naive Bayes classifier
Decision Tree classifier
Random Forest classifier
support vector machine
Decision Tree Regression
Random Forest Regression