supervised machine learning

What is supervised machine learning?

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

A supervised learning algorithm learns from labeled training data helping you predict results for unexpected data.

Building scaling and deploying accurate models of supervised and unsupervised machine learning requires time and technical expertise from a team of highly skilled data scientist algorithms. 

Furthermore, the data scientist must rebuild models to ensure that the insights provided remain correct until its data changes.

Advantages of Supervised Learning

• Supervised allows you to collect data or generate data output from previous experience

• Helps you optimize performance criteria through experience

• Supervised machine learning helps you solve different types of real-world calculation problems.

Disadvantages of supervised learning

• The decision limit may be overtrained if your training set does not have supervised learning example you want to have in class

• You need to pick lots of good examples from each class while you train the classifier.

• Classifying big data can be a real challenge.

• Supervised learning training requires a lot of calculation time.

How supervised learning works

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

  • Weather conditions
  • Time of day
  • Holidays

All of these details are your inputs. Output is the length of time it took to return home on that specific day.

You know instinctively that if it rains outside it will take you longer to drive home. But the machine needs data and statistics.

Let's now see how you can develop a supervised and unsupervised machine learning algorithms model of this example that will help the user determine travel time. The first thing you need to create is a training set. This training set will contain the total daily travel time and the appropriate factors such as weather time etc. 

Based on this training kit your machine may see that there is a direct relationship between the amount of rain and the time it will take you to get home.

So it makes sure that the more it rains the longer you will travel back to your home. It may also see the connection between the time you leave work and the time you will be on the road.

The closer you are to 6 p.m. The longer it takes you to get home. Your machine may find some connections with your flagged data.

This is the beginning of your data model. This is starting to affect the effect of rain on the way people drive. It is also starting to see more people traveling at a certain time of day.

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