What is Supervised Learning? Understanding the Basics of ML

Supervised learning is a type of machine learning in which an algorithm is trained on a labeled dataset to make predictions or take actions. The goal of the algorithm is to learn a mapping from input data to output labels, such that it can make accurate predictions on new, unseen data.

Types of Supervised Learning

There are two main types of supervised learning: classification and regression.

Classification algorithms are used to predict categorical labels, such as whether an email is spam or not.

Regression algorithms, on the other hand, are used to predict continuous values, such as the price of a house.

The Training Process

The training process for a supervised learning algorithm involves providing it with a labeled dataset, which it uses to learn the mapping from input data to output labels.

The dataset is typically split into two parts: a training set and a test set.

The algorithm is first trained on the training set and then evaluated on the test set to measure its performance.

The performance of a supervised learning algorithm is typically measured using metrics such as accuracy, precision, and recall.

These metrics allow us to evaluate the algorithm’s ability to make correct predictions and identify any areas where it is performing poorly.

Choosing an Algorithm

There are many different supervised learning algorithms to choose from, each with its strengths and weaknesses. The choice of algorithm will depend on the specific problem you are trying to solve, as well as the characteristics of your dataset.

Some popular supervised learning algorithms include:

• k-Nearest Neighbors (k-NN)
• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines (SVMs)
• Neural Networks

Conclusion

Supervised learning is a powerful technique for making predictions and taking actions based on input data.

By providing an algorithm with a labeled dataset, we can train it to learn a mapping from input data to output labels, such that it can make accurate predictions on new, unseen data.

There are many different algorithms to choose from, each with its strengths and weaknesses, so it is important to carefully evaluate the problem and dataset before selecting an algorithm.