Introduction to Supervised Learning

Supervised Learning is the common sub-branch of machine learning. In Supervised Learning, we train the model with labelled datasets i.e. we have both raw dataset and its results. Then data is splitter into two parts training datasets and test datasets where training datasets are used to train the model while test datasets are used as new datasets for predicting the results or to make the model more accurate.

While training the model with a supervised learning algorithm the training data has inputs paired with correct outputs. During training, the model searches for a pattern in data that correlates with the outputs. After the process of training, we take test datasets and will determine which label the new inputs will be classified as based on prior training data.

Supervised machine learning algorithms are designed to learn by example. The name “supervised” learning originates from the idea that training this type of algorithm is like having a teacher supervise the whole process. The model performs fast because the training time taken is less as we already have desired results in our dataset.

This model predicts accurate results on unseen data or new data without even knowing a prior target. In some of the supervised learning models, we revert the output result to learn more to achieve the highest possible accuracy.

Use Cases

Spam detection: This is used for detecting spam mails by using different keywords and content and feedback given by its users. It uses supervised learning methods.
Bioinformatics: It stores the biological information of humans like fingertips, iris textures, eyes, swabs, and so on. Every smart device asks to authenticate either through fingertips or facial recognition.
Object Recognitions: It is another very popular application of supervised learning. It is where you have to choose multiple images as per the instruction to get confirmed that you are a human.

Disadvantages

Its computation time is vast.
It can easily be overfitted.
Pre-processing of data is a big challenge.
It needs to be updated always.

It uses cases that use labelled data to train a machine. Supervised learning requires experienced data scientists to build, scale, and update the models. If the algorithm goes wrong result will also be inaccurate. Thus, the selection of data is crucial for supervised learning to work efficiently.