Supervised v/s Unsupervised Learning – 4 Key Differences

Filed Under: Machine Learning
Supervised V S Unsupervised

Hello readers! In our series of Machine Learning concepts, today we will be focusing on Supervised v/s Unsupervised Learning in detail.

So, let us begin!!


Supervised Machine Learning – Overview

In Machine Learning, we come across a variety of algorithms. Supervised Machine Learning is one such type of it.

In Supervised Machine Learning algorithms, we develop models that learn from the data we feed them. That is, we provide these models with some historic data values.

It learns from these data values and detects the presence of certain patterns from the dataset. Further, it makes use of these patterns to make predictions on real life problems.

Some prominently used Supervised Machine Learning models–

  1. Linear Regression
  2. Decision Trees
  3. Knn
  4. Na茂ve Bayes
  5. Random Forest

Unsupervised Machine Learning – Overview

Moving to Unsupervised ML algorithms, the best thing which differentiates it from other types of algorithms is that it does not make use of historic data to make predictions.

Rather, it makes use of unlabeled data to discover and detect the patterns in the category of the data values.

Mostly used Unsupervised Machine Learning algorithms:

  1. K means clustering
  2. Association Algorithms

Supervised v/s Unsupervised ML – Get set GO!

Having understood about Supervised and Unsupervised Machine Learning algorithms and its variety, let us know focus on some of the key aspects when it comes to differences between Supervised and Unsupervised models.

So, let us begin!!


1. Input dataset

In Supervised Learning, we make use of labeled data to detect patterns. That is we feed the algorithm with the data that is labeled as to its identity for it to understand and learn.

In Unsupervised Machine Learning, we feed an unlabeled data set to the algorithm. It does not depend on the training data to detect the patterns.

2. Evaluation of the model

When it comes to evaluating the model in terms of accuracy, Supervised Machine Learning models give better results with higher accuracy as compared to Unsupervised learning models.

Because Supervised models make use of historic data which makes it more accurate in terms of predictions. On the other hand, in Unsupervised Machine Learning models, no historic or learning data is fed to the model which makes it difficult to get more efficient results based on understanding.

3. Computational Complexity

In terms of computing capability and complexity, Supervised models have an upper hand as the algorithms are simpler compared to Unsupervised models.

The reason is that, in Unsupervised Learning models, no learning data is provided, thus it makes use of clustering or aggregation to understand the similarities which is a complex task.

4. Behavior with input data

In Supervised Learning models, before processing the test data, the model is very well aware of the input data as well as the output values of the same.

On the contrary, in Unsupervised learning models, only the input data is fed to the model i.e. the outcome of the prediction is unknown to the model irrespective of the data elements.


Conclusion

By this, we have come to the end of this topic. Feel free to comment below, in case you come across any question.

Do let us know your understanding about the key differences between Supervised and Unsupervised models in the comment section.

For more such posts related to Machine Learning, Stay tuned and till then,

Happy Learning!! 馃檪

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