Supervised learning

1. What’s supervised learning?

Supervised learning is a training approach in machine learning where a model is trained using a labeled data set. In this type of learning, the algorithm is provided with a set of input examples along with the corresponding desired outputs. The goal is for the model to learn the relationship between inputs and outputs, and then be able to make accurate predictions or classifications on new or previously unseen data.

2. What’s the supervised learning function?

Supervised learning plays a critical role in training machine learning algorithms to perform classification and prediction tasks accurately. Its usefulness lies in its ability to learn complex patterns from labeled data, where the model is provided with input examples along with corresponding desired outputs. This allows the model to generalize and make accurate predictions on new, unseen data. Various applications, such as facial recognition, fraud detection in financial transactions, and personalization of recommendations on online platforms, benefit from supervised learning. By training models to understand the relationship between input characteristics and expected outputs, this approach drives advances in fields ranging from healthcare to industrial automation, significantly improving the ability of machines to perform complex tasks and make informed decisions.

3. Examples of supervised learning

• Decision trees.
• Linear and logistic regression.
• Support Vector Machine (SVM).
• Random forest.
• K Nearest Neigtbors (KNN).
• CNN.
• RNN.