Supervised and Unsupervised Learning

Both Supervised and Unsupervised Learning are broad categories of Machine Learning. Unsupervised Learning refers to algorithms that are capable of performing pattern analysis and data mining on a set of input data free from any reference input. Unsupervised learning algorithms are best at analysing unlabelled or incomplete datasets, and when there is the absence of sufficient information to make decisions.

Supervised Learning

Supervised Learning is a machine learning task where algorithms are trained on a set of labelled training data consisting of input variables and their corresponding output variables, to use the discovered patterns for future data classification.

A supervised learning algorithm deduces a function from the given training information to predict an output from new data.

Supervised learning algorithms are suitable for classification, regression, and other numerical applications.

Benefits of Supervised Learning

1) Supervised Learning provides us with the best option to predict a value from new data.

2) Supervised Learning provides us with very robust models and we can use these models for a vast number of tasks and applications.

3) Unlike Unsupervised Learning, Supervised Learning algorithms are scalable and can be applied to a wide variety of problems.

Here are some examples of supervised learning algorithms:

Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, SVM-Kernel Methods, etc.

Unsupervised Learning

In contrast to supervised learning, unsupervised learning algorithms are incapable of making any predictions of the output variables from new data.

Unsupervised learning algorithms do not require labelled or known input variables to be executed.

Unlike supervised methods, unsupervised methods can be applied to a large variety of problems.

Benefits of Unsupervised Learning

1) Unsupervised Learning provides us with powerful algorithms that can analyse a large variety of data sets.

2) Unsupervised Learning deals with unlabelled data, thereby making the problem very similar to that of pattern recognition.

3) Unlike supervised methods, unsupervised methods are good at dealing with missing or sparse data as well.

Many times, when input variables are very diverse and there is no specific “look-up table” to learn from the dataset, we consider using an algorithm based on clustering methods and learn which groups of input variables are related to each other.

Differences between Supervised and Unsupervised learning

1) Supervised Learning and Unsupervised Learning are different in many aspects.

2) Supervised Learning is best for time series as mentioned above, which can be easily interpreted as time series machine learning.

3) Unsupervised Learning deals with data sets, which are not easily interpretable and can be represented as a matrix of values.

4) Because Unsupervised learning algorithms deal with unlabelled data, they are also good at finding important patterns and associations by dividing the input data into various clusters. These clusters can then be used for further analysis or decision-making.

5) Unsupervised learning algorithms find correlations between all possible variables within a dataset; thus we call it “uncorrelated” learning.

6) Unsupervised Learning is a perfect solution to data mining in the absence of labelled or known input variables.

7) The biggest difference between Supervised and Unsupervised Learning is that supervised learning takes in the labelled training data and outputs a function that can be used for future prediction, while unsupervised algorithms do not require any training data or features.

Conclusion

Supervised and Unsupervised Learning both have their strengths and weaknesses, but they are both important to classify unknown or unlabeled data. Each algorithm has its own set of characteristics – one that is more suited towards solving a particular problem.

The most important aspect of any machine learning algorithm, whether it is supervised or unsupervised, is the ability to adapt new information and continuously learn from the provided data. Supervised and Unsupervised Learning are two sides of the same coin – both with their benefits and applications.