Supervised Learning

In supervised learning, we train the machine using data which is well labelled. It means some data is already tagged with the correct answer.

Types of Supervised Machine Learning Algorithms:

Regression (Linear and Multi-linear)
Logistic Regression
Classification
Naïve Bayes Classifiers
Decision Trees
Support Vector Machine (SVM)

Challenges in Supervised machine learning:

Irrelevant input feature present training data could give inaccurate or wrong 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 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 over-trained 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.

Figure 1: Supervised Learning

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