Understanding Unsupervised Learning and How It Is Different From Supervised Learning

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The computer scientist Yann LeCun famously said that “if intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake.” In other words, there is a huge potential in unsupervised learning that we have only barely started to sink our teeth into.

What is Unsupervised Learning?

Unsupervised learning is a type of machine learning algorithm that looks for patterns in a dataset without pre-existing labels. As the name suggests, this type of machine learning is unsupervised and requires little human supervision and prep work. Because unsupervised learning does not rely on labels to identify patterns, the insights tend to be less biased than other forms of AI.

How is Unsupervised Learning Used?

1. Dimensionality Reduction: This machine learning technique is used when the number of features in a dataset is too high. This technique reduces the number of inputs into a more manageable size all while preserving the data integrity.
2. Clustering: The goal is to group similar instances together into clusters. Clustering is great tool for Data Analysis, Customer Segmentation, Recommendation System, Search Engine, Image Segmentation etc.
3. Anomaly Detection: The objective is to learn normal data looks like, and then use that to detect abnormal instance, such as detective items on a production line or a new trend in a time series.
4. Density Estimation: This is the task of estimating the probability density function of the random process that generated the dataset. Density estimation is commonly used for anomaly detection: instance located in very low-density regions are likely to be anomalies. It is also useful for data analysis and visualizations.

What are the Benefits of Unsupervised Learning?

Using unsupervised machine learning algorithms on your data has many benefits. Here are some of the most common reasons people turn to unsupervised learning:

• It can handle large amounts of unlabeled and unstructured data.
• It makes it easier and faster to analyze complex data.
• It is able to identify previously undetected patterns.
• It learns about your data so it can teach you what you don’t know.

Disadvantages of Unsupervised Learning

• You cannot get precise records regarding records sorting, and the output as information utilized in unsupervised knowledge is labelled and not acknowledged.
• Less accuracy of the effects is because the enter records are not acknowledged and now not categorized through humans earlier. This approach that the device requires to do this itself.
• The spectral instructions do no longer always correspond to informational classes.
• The user desires to spend time decoding and label the instructions which comply with that type.
• Spectral houses of training also can alternate through the years so that you can not have the same class records whilst transferring from one photograph to every other.

The main difference between supervised and unsupervised learning: Labeled data

The main distinction between the two approaches is the use of labeled datasets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not.

In supervised learning, the algorithm learns from the training dataset by iteratively making predictions on the data and adjusting for the correct answer. While supervised learning models tend to be more accurate than unsupervised learning models, they require upfront human intervention to label the data appropriately. For example, a supervised learning model can predict how long your commute will be based on the time of day, weather conditions and so on. But first, you’ll have to train it to know that rainy weather extends the driving time.

Unsupervised learning models, in contrast, work on their own to discover the inherent structure of unlabeled data. Note that they still require some human intervention for validating output variables. For example, an unsupervised learning model can identify that online shoppers often purchase groups of products at the same time. However, a data analyst would need to validate that it makes sense for a recommendation engine to group baby clothes with an order of diapers, applesauce and sippy cups.

Other key differences between supervised and unsupervised learning

• Goals: In supervised learning, the goal is to predict outcomes for new data. You know up front the type of results to expect. With an unsupervised learning algorithm, the goal is to get insights from large volumes of new data. The machine learning itself determines what is different or interesting from the dataset.
• Applications: Supervised learning models are ideal for spam detection, sentiment analysis, weather forecasting and pricing predictions, among other things. In contrast, unsupervised learning is a great fit for anomaly detection, recommendation engines, customer personas and medical imaging.
• Complexity: Supervised learning is a simple method for machine learning, typically calculated through the use of programs like R or Python. In unsupervised learning, you need powerful tools for working with large amounts of unclassified data. Unsupervised learning models are computationally complex because they need a large training set to produce intended outcomes.
• Drawbacks: Supervised learning models can be time-consuming to train, and the labels for input and output variables require expertise. Meanwhile, unsupervised learning methods can have wildly inaccurate results unless you have human intervention to validate the output variables.

Supervised vs. unsupervised learning: Which is best for you?

Choosing the right approach for your situation depends on how your data scientists assess the structure and volume of your data, as well as the use case. To make your decision, be sure to do the following:

• Evaluate your input data: Is it labeled or unlabeled data? Do you have experts that can support additional labeling?
• Define your goals: Do you have a recurring, well-defined problem to solve? Or will the algorithm need to predict new problems?
• Review your options for algorithms: Are there algorithms with the same dimensionality you need (number of features, attributes or characteristics)? Can they support your data volume and structure?

Semi-supervised learning: The best of both worlds

Can’t decide on whether to use supervised or unsupervised learning? Semi-supervised learning is a happy medium, where you use a training dataset with both labeled and unlabeled data. It’s particularly useful when it’s difficult to extract relevant features from data — and when you have a high volume of data.

Semi-supervised learning is ideal for medical images, where a small amount of training data can lead to a significant improvement in accuracy. For example: a radiologist can label a small subset of CT scans for tumors or diseases so the machine can more accurately predict which patients might require more medical attention.

Outgo and Resources for further Experiment.

If you Liked This Article and you have some doubt and you want the Brief Explanation then Consider Checking IBM Article on Unsupervised Learning. I took some points from the IBM Article to make this article so i thanks IBM to let me use there resources for my research.

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