In machine learning, supervised learning is characterized by the concept of a supervisor whose goal is to provide an accurate measure of error that is directly comparable to the output values. If you don’t know in what kinds of problems supervised machine learning is used, this article is for you, because here I will walk you through the applications of supervised learning.
What is Supervised Learning?
In machine learning, supervised learning is characterized by the concept of a supervisor whose goal is to provide an accurate measurement of the error that is directly comparable to the output values. With this information, we can correct the parameters to reduce the magnitude of the loss function. So, if the algorithm is flexible enough and the data points are consistent, the overall accuracy increases and the difference between predicted and expected values reaches close to zero. This is how supervised learning works.
In supervised learning, we know the expected values before training a model, but our goal is to train a machine learning model that can also work well on samples that we have never seen before. So, in supervised learning, we need to allow the model to develop a generalization ability to avoid a common problem known as overfitting. Hope you now understand what supervised learning is in machine learning. Now in the section below, I will introduce you to the common applications where supervised learning is used.
Applications of Supervised Learning
In machine learning, some of the common applications of supervised learning are:
- Predictive analysis based on the problems of regression and classification.
- Spam Detection
- Pattern Detection
- Natural Language Processing
- Sentiment Analysis
- Image Classification
- Sequence Processing
So these were some of the common problems where supervised learning is used in machine learning. I hope you liked this article on the applications of supervised learning. Feel free to ask your valuable questions in the comments section below.
