Advantages of Supervised Learning
The power of supervised learning lies in its ability to accurately predict patterns and make data-driven decisions across a variety of applications. Here are some advantages listed below:
- Labeled training data benefits supervised learning by enabling models to accurately learn patterns and relationships between inputs and outputs.
- Supervised learning models can accurately predict and classify new data.
- Supervised learning has a wide range of applications, including classification, regression, and even more complex problems like image recognition and natural language processing.
- Well-established evaluation metrics, including accuracy, precision, recall, and F1-score, facilitate the assessment of supervised learning model performance.
Supervised Machine Learning
A machine is said to be learning from past Experiences(data feed-in) with respect to some class of tasks if its Performance in a given Task improves with the Experience. For example, assume that a machine has to predict whether a customer will buy a specific product let’s say “Antivirus” this year or not. The machine will do it by looking at the previous knowledge/past experiences i.e. the data of products that the customer had bought every year and if he buys an Antivirus every year, then there is a high probability that the customer is going to buy an antivirus this year as well. This is how machine learning works at the basic conceptual level.