Semi-supervised learning is a combination of supervised and unsupervised machine learning. The issues here are based on datasets where the dataset will contain many unlabeled instances and a few labelled instances. In this article, I will take you through what semi-supervised learning is in machine learning.
Semi-supervised Learning in Machine Learning
When instances are labelled, this is the problem with supervised learning, when instances are not labelled, it is because the problem is based on unsupervised learning. But because the process of labelling the data is very long and expensive, there are situations where the dataset comes with many unlabeled instances and a few labelled instances. This is what semi-supervised learning is based on. Simply put, it is a combination of supervised and unsupervised machine learning.
The cost of labelling data is also a complex task which is why every company tries to reduce the cost by using the concept of pf semi-supervised techniques. Below is the pricing of the Google cloud platform for labelling the data which will help you to get an idea of how much data labelling costs.
Semi-supervised learning was introduced to overcome the drawbacks of supervised and unsupervised machine learning techniques. Supervised learning requires a lot of training data to classify the test data, which is very expensive and time-consuming. On the other hand, the main disadvantage of unsupervised learning is that it cannot accurately group unknown data (test data). So, to overcome these drawbacks, the concept of semi-supervised learning has been introduced which can learn from a small amount of data and it can also cluster data on unknown data.
Semi-supervised learning is further divided into two categories:
- Semi-supervised Classification
- Semi-supervised Clustering
The semi-supervised classification approach is similar to supervised learning. The difference is that in supervised learning, we use a lot of training data to categorize the test data, which is very expensive and time-consuming. But on the other hand, in semi-supervised classification, we use a relatively small amount of training data, which reduces the cost of labelling instances and the time to prepare the dataset.
Semi-supervised clustering is very similar to unsupervised learning. In unsupervised learning, we use unlabelled data for clustering. But in semi-supervised clustering, we use both the labelled instances and unlabeled instances to create clusters.
Semi-supervised is a combination of supervised and unsupervised machine learning techniques. This concept was introduced to overcome the drawbacks of supervised and unsupervised machine learning techniques and also gives greater precision by training models in the combination of labelled and unlabeled instances.
I hope you liked this article on what is semisupervised learning in machine learning. Feel free to ask your valuable questions in the comments section below.