Supervised machine learning is one of the most commonly used and successful types of machine learning. In this article, I will describe supervised learning in detail and explain the types of supervised learning algorithms.
Introduction to Supervised Learning
The most effective machine learning algorithms are considered those that can automate the decision-making processes by generalizing from known samples. This is called supervised learning, where the user supplies the algorithm with pairs of desired inputs and outputs and the algorithm finds a way to produce the desired output given an input.
Put simply, supervised learning can create an exit for an entrance that he has never seen before without the help of a human.
What are Supervised Learning Algorithms?
Machine learning algorithms that learn from input/output pairs are called supervised learning algorithms because a “teacher” oversees the algorithms in the form of the desired outputs for each example they learn.
Although creating an input and output dataset is often considered as a manual process, while the supervised learning algorithms can be easily understood and their performance is very easy to measure. If your app can be framed as a supervised learning problem, and you are able to create a dataset that includes the desired outcome, machine learning will likely be able to solve your problem.
When Supervised Learning is Used?
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 make up our training set.
Our goal is to make accurate predictions for new, unpublished data. The Supervised machine learning algorithms require some human effort to build and process the training set, but then it automates and speeds up all the laborious or impractical tasks.
Classification and Regression
There are two main types of supervised machine learning problems called classification and regression.
The goal in classification is to predict a class label from a predefined list of possibilities. Classification is sometimes separated into binary classification, which is the special case of distinguishing between exactly two classes, and multiclass classification, which is the classification between more than two classes.
You can think of binary classification as an attempt to answer a yes / no question. An example of a binary classification problem is whether or not to classify emails as spam or not. In this binary classification task, the yes / no question asked would be “Is this spam?”
In binary classification, we often speak of one class being a positive class and the other class being the negative class. Here, the positive does not represent a benefit or a value, but rather the object of the study. So, while looking for positive could mean that we are looking for the spam class. Which of the two classes is said to be positive is often a subjective and domain-specific question.
An example of a multiclass classification is to predict the language in which a website is located from the website text. The class labels here will most probably 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 (or a real number in mathematical terms). Predicting a person’s annual income from education, age, and location is an example of a regression task.
When predicting income, the predicted value is an amount and can be any number within a given range. Another example of a regression task is to predict the performance of a corn farm based on attributes such as past yields, weather conditions, and the number of employees working on the farm. The yield can again be an arbitrary number.
Difference Between Classification and Regression
An easy way to distinguish between classification and regression tasks is to ask if there is some kind of continuity in the output. If there is continuity between the possible outcomes, then the problem is a regression problem. Consider planning annual income.
There is a clear continuity in the output. Whether a person earns $ 40,000 or $ 40,001 per year does not make a tangible difference, even if they are different amounts; if our algorithm predicts $ 39,999 or $ 40,001 when it should have predicted $ 40,000, we don’t mind much.
In contrast, for the task of recognizing the language of a website (which is a classification problem), there is no question of degree. A website is in one language or another. There is no continuity between languages, and there is no language between English and French.
I hope you liked this article on supervised learning in machine learning. Feel free to ask your valuable questions in the comments section below.