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Table of contents
1.
Introduction
2.
Types of Unsupervised Learning
3.
Applications of unsupervised learning
4.
Drawbacks
5.
FAQs
6.
Key Takeaways
Last Updated: Mar 27, 2024
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Drawbacks of Unsupervised Learning

Author Prakriti
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Ashwin Goyal
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Introduction

Machine Learning is primarily divided into supervised learning, Unsupervised Learning, and Reinforcement Learning. In this article, we will discuss unsupervised learning. As the name suggests, unsupervised learning doesn't need a supervisor to train the model. In unsupervised learning, the model doesn't get the target variable. It tries to find out patterns in the data. For example, we can use unsupervised learning to separate blue and black pens mixed in a bag. 

Types of Unsupervised Learning

  • Parametrized Unsupervised Learning
    Here, we assume that our data follows a probability distribution based on a fixed set of parameters. So, according to theory, in a normal distribution family, all the members are parameterized by mean and standard deviation and have the same shape. This learning uses Gaussian mixture models and expectation-maximization algorithms to predict data labels. It is tough to check the results as we do not know target labels.
  • Non-parametrized Unsupervised Learning
    Here, we do not assume that data follows any probability distribution, and this method is also known as the distribution-free method. Here, the data is grouped into clusters such that each cluster tells about the class of the data.
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Applications of unsupervised learning

The major applications of unsupervised learning are visualization, clustering, dimensionality reduction, anomaly detection, etc.

  • Visualization
    As the name suggests, visualization means visualizing the data, i.e., creating diagrams, charts, figures, etc. 
    t-distributed Stochastic Neighbor Embedding (t-SNE) is a famous algorithm for visualization.
  • Clustering
    Here, we try to group the data into clusters such that similar things are close and dissimilar things are far apart.
    For example: if you have a youtube channel, you can infer meaningful information about your subscribers using unsupervised learning. You can, for example, conclude that 90% of your subscribers are from India and 10% from the USA. You can now use this information to make videos to attract people from other countries.
    Some standard clustering algorithms are k-means, expectation-maximization, etc.
  • Dimensionality reduction
    Here, we try to reduce the number of features present in our data. Sometimes datasets have thousands of features that make the model's training slower. In that case, we can use dimensionality reduction to reduce the number of features. For example, if some features are very similar, we can group them into one feature. Principal Component Analysis (PCA) is one of the majorly used algorithms for dimensionality reduction.
  •  Anomaly Detection
    Here, we try to identify unusual events or observations. For example, credit card fraud detection. Here, if the amount being transferred is higher than usual, then the person gets an alert whether the transaction is desired or not to prevent fraud.

Drawbacks

You might think that unsupervised learning is excellent as it doesn't need labeled training data, but it is not the case every time!

  • Unsupervised learning models are computationally expensive.
  • In unsupervised learning, the models learn and observe patterns from raw data without any training labels or prior knowledge. The learning takes a lot of time as it has to check all possibilities.
  • The results obtained from unsupervised learning are not always useful as we do not have any label or target to confirm its usefulness.
  • The spectral classes don't always correspond to informational classes.
  • It can be costlier as often human intervention is required to correlate the patterns obtained to the domain knowledge.
  • We cannot get precise knowledge on the sorting and output of unsupervised learning models. It highly depends on the model, which depends on the machine.
  • The results can have less accuracy as we do not have labeled training data.

FAQs

1. What is unsupervised learning?

Unsupervised learning is the technique in which the model tries to find patterns in the data without having labeled training data.

2. What is one of the major drawbacks of unsupervised learning?

One of the major drawbacks of unsupervised learning is that we cannot get precise information on the data sorting.

3. What are the drawbacks of machine learning?

Machine learning requires data to train on, and collecting data is not easy.

4. How is unsupervised learning related to clustering?

Unsupervised learning finds patterns in data to group similar things together and hence is related to clustering.

5. Why is unsupervised learning important?

Unsupervised learning is important as we can extract useful information from unlabeled training data by finding patterns.

Key Takeaways

This article discussed the drawbacks of unsupervised learning.

You can also consider our Machine Learning Course to give your career an edge over others.

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