Supervised learning (SL) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.

Simply put the supervised learning technique is similar to teaching a child showing it an apple and introducing the child with it and saying this is an apple. So exactly what is going on with machines is that we are teaching them. Let us assume you are giving some labeled data ( an apple with its shape and colour) to your machine and you will say it to remember it or to store this info in its memory. After that machine will remember it and when you show any picture of an apple it will scan the picture and give the result that “This is an apple”

Diagram:

Now let’s see the algorithms which is used in Supervised Learning:

Algorithms

The most widely used learning algorithms are:

Support vector machines (SVMs):

Support vector machines are a set of supervised learning methods used for classification, regression and outliers detection. Uses:

• image classification

Linear regression:

· Linear regressions can be used in business to evaluate trends and make estimates or forecasts.

Logistic regression:

· Logistic regression analysis is used to examine the association of (categorical or continuous) independent variable(s) with one dichotomous dependent variable. This is in contrast to linear regression analysis in which the dependent variable is a continuous variable.

Naive Bayes:

· As this algorithm is fast and efficient, you can use it to make real-time predictions. This algorithm is popular for multi-class predictions. You can find the probability of multiple target classes easily by using this algorithm. Linear discriminant analysis · Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher’s linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events.

Decision trees:

· Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, the decision tree algorithm can be used for solving regression and classification problems too.

K-nearest neighbor algorithm:

The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems.

Neural networks (Multilayer perceptron):

· Multilayer perceptron classical neural networks are used for basic operations like data visualization, data compression, and encryption. It is more of a practical swiss army knife tool to do the dirty work Similarity learning · Similarity learning is used in information retrieval for learning to rank, in face verification or face identification, and in recommendation systems. Also, many machine learning approaches rely on some metric.

Benefits 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.