Machine Learning approaches can be either Supervised or Unsupervised. If you can anticipate the expanse of data, and if it is possible to divide the data into categories, then the best approach is to help the algorithm become smarter by Supervised Learning.
If you anticipate that the amount of data is massive, and if you think that the data cannot be simply classified or labelled, then it is better to go for Unsupervised Learning approach and let the algorithms handle predictions smartly.
Differences between Supervised and Unsupervised Machine Learning
The table below shows some key differences between supervised and unsupervised machine learning −
| Supervised Technique | Unsupervised Technique |
|---|---|
| Supervised machine learning algorithms are trained using both training data and its associated output i.e., label data. | Unsupervised machine learning algorithms do not require labeled data for training. |
| Supervised machine learning model learns the association between input training data and their labels. | Unsupervised machine learning model learns the pattern and relationship from the given raw data. |
| Supervised ML model takes feedback to check whether it is predicting the correct output or not. | Unsupervised ML model does not take any kind of feedback. |
| As name entails, supervised machine learning algorithms needs supervision to train the model. | As name entails, unsupervised machine learning algorithms does not any kind of supervision to train the model. |
| We can divide supervised machine learning algorithms in two broad classes namely Classification and Regression. | Clustering, Anomaly Detection, Association, and Association are some of the broad classed of unsupervised machine learning algorithms. |
| In terms of computational complexity, supervised machine learning methods are computationally simple. | Unsupervised machine learning methods are computationally complex. |
| Supervised machine learning methods are highly accurate. | Unsupervised machine learning methods are less accurate. |
| In supervised machine learning, the learning takes place offline. | In unsupervised machine learning, the learning takes place in real time. |
| Number of classes is already known before implementing supervised machine learning methods. | In unsupervised learning methods, number of classes are not known in prior. |
| One of the main drawbacks of supervised learning is to classify big data. | As the data used in unsupervised learning is not labeled, getting precise information regarding data sorting is one of the main drawbacks of it. |
| Some of the well-known supervised machine learning algorithms are KNN (k-nearest neighbors), Decision tree, Logistic Regression, and Random Forest. | Some of the well-known unsupervised machine learning algorithms are Hebbian Learning, K-means Clustering, and Hierarchical Clustering. |