What is Supervised Machine Learning?

What is Supervised Machine Learning?

In Supervised learning, you train the machine using data that is well "labeled." It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.

A supervised learning algorithm learns from labeled training data, helps you to predict outcomes for unforeseen data.

Successfully building, scaling, and deploying accurate supervised machine learning models takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientist must rebuild models to make sure the insights given remains true until its data changes.

How Supervised Learning Works

For example, you want to train a machine to help you predict how long it will take you to drive home from your workplace. Here, you start by creating a set of labeled data. This data includes

• Weather conditions
• Time of the day
• Holidays

All these details are your inputs. The output is the amount of time it took to drive back home on that specific day.

You instinctively know that if it's raining outside, then it will take you longer to drive home. But the machine needs data and statistics.

Let's see now how you can develop a supervised learning model of this example which help the user to determine the commute time. The first thing you requires to create is a training set. This training set will contain the total commute time and corresponding factors like weather, time, etc. Based on this training set, your machine might see there's a direct relationship between the amount of rain and time you will take to get home.

So, it ascertains that the more it rains, the longer you will be driving to get back to your home. It might also see the connection between the time you leave work and the time you'll be on the road.

The closer you're to 6 p.m. the longer it takes for you to get home. Your machine may find some of the relationships with your labeled data.

Types of Supervised Machine Learning Algorithms

• Regression:
• Logistic Regression:
• Classification:
• Naïve Bayes Classifiers
• Decision Trees
• Support Vector Machine
• Supervised vs. Unsupervised Machine learning techniques

Based On	Supervised machine learning technique	Unsupervised machine learning technique
Input Data	Algorithms are trained using labeled data.	Algorithms are used against data which is not labelled
Computational Complexity	Supervised learning is a simpler method.	Unsupervised learning is computationally complex
Accuracy	Highly accurate and trustworthy method.	Less accurate and trustworthy method.

Challenges in Supervised machine learning

• Irrelevant input feature present training data could give inaccurate results

• Data preparation and pre-processing is always a challenge.

• Accuracy suffers when impossible, unlikely, and incomplete values have been inputted as training data

• If the concerned expert is not available, then the other approach is "brute-force." It means you need to think that the right features (input variables) to train the machine on. It could be inaccurate.

Advantages of Supervised Learning:

• Supervised learning allows you to collect data or produce a data output from the previous experience

• Helps you to optimize performance criteria using experience

• Supervised machine learning helps you to solve various types of real-world computation problems.

Disadvantages of Supervised Learning

• Decision boundary might be overtrained if your training set which doesn't have examples that you want to have in a class

• You need to select lots of good examples from each class while you are training the classifier.

• Classifying big data can be a real challenge.

• Training for supervised learning needs a lot of computation time.

Best practices for Supervised Learning

• Before doing anything else, you need to decide what kind of data is to be used as a training set

• You need to decide the structure of the learned function and learning algorithm.

• Gathere corresponding outputs either from human experts or from measurements

Best practices for Supervised Learning

• Before doing anything else, you need to decide what kind of data is to be used as a training set
• You need to decide the structure of the learned function and learning algorithm.
• Gathere corresponding outputs either from human experts or from measurements

Summary

• In Supervised learning, you train the machine using data which is well "labelled."
• You want to train a machine which helps you predict how long it will take you to drive home from your workplace is an example of supervised learning
• Regression and Classification are two types of supervised machine learning techniques.
• Supervised learning is a simpler method while Unsupervised learning is a complex method.
• The biggest challenge in supervised learning is that Irrelevant input feature present training data could give inaccurate results.
• The main advantage of supervised learning is that it allows you to collect data or produce a data output from the previous experience.
• The drawback of this model is that decision boundary might be overstrained if your training set doesn't have examples that you want to have in a class.
• As a best practice of supervise learning, you first need to decide what kind of data should be used as a training set.