Have you heard about Siri, Alexa or Google Now? Have you ever used a face-recognition feature in your smartphone? Well, all these are a few examples of Machine Learning. The technology world has seen some tremendous changes in the past two decades. With every changing technology, consumer expectations are increasing rapidly. To keep up the pace with growing customer expectations, companies are using Machine learning to make life simpler. There are two main types of machine learning: Supervised and Unsupervised Machine Learning. If you are embarking on your Machine Learning journey, the first thing you should do is learn how these two tasks are different from each other.
The main difference between Supervised and Unsupervised Machine Learning is that the former works on labeled outputs and the latter does not.
Ksolves brings to you this detailed article where we will discuss the two concepts and help you decide when to use what? Let us walk you through both the approaches in detail.
What is Supervised Machine Learning?
Supervised Machine Learning is an approach defined by the use of labeled datasets. This means that some data is already tagged and can be compared to learning in the presence of a supervisor, unlike Unsupervised Machine Learning. These datasets are designed in such a way that they can train or supervise algorithms into classifying data and predicting accurate outcomes.
We can separate Supervised Learning into two types when data mining. They are: Classification and Regression. Let’s shed a light on them.
- Classification: This basically means to group the outputs in a class. Classification problems generally use an algorithm to assign test data with accuracy into specific categories. It is also used to classify spam in a different folder that is separate from your inbox. Few common types of classification algorithms are- Linear classifiers, decision trees, support vector machine and random forest.
- Regression: It is a type of Supervised Learning that uses an algorithm and understands the relationship between independent and dependent variables. It is used for problems where the output variable is a real value. It is helpful in predicting numerical values on different data points. Few commonly used regression algorithms are linear regression, logistic regression, and polynomial regression.
What is Unsupervised Machine Learning?
Unsupervised Machine Learning is a type of machine learning where there is no need to supervise the model, instead it works on its own to collect information. Unsupervised Learning uses algorithms to analyze and cluster unlabelled datasets. They are called unsupervised as they discover hidden patterns in data without any human intervention.
Unsupervised Machine Learning models can be divided into three main tasks: Clustering, association and dimension reduction. Let’s discuss in detail.
- Clustering: It is a data mining technique where unlabelled data is grouped based on their similarities and differences. This technique is helpful for market segmentation, image compression and many more.
- Association: It is used to find relationships between variables using different rules. These methods are often used for market basket analysis.
- Dimensionality reduction: This technique is used when the number of dimensions in a dataset is too high. It reduces the size of the data inputs without compromising the data integrity.
Now that we have understood what these two algorithms are, let’s dig deep and discuss how they are different.
Supervised vs. Unsupervised Machine Learning
Like we said earlier, the main difference between both the approaches is mainly the use of labeled data.
In supervised learning, the algorithm learns from training datasets by making predictions for data and adjusting the answer. Supervised learning models are more accurate but require human intervention to label the data.
Unsupervised Machine Learning on the other hand works on their own to collect the unlabeled data and will need some human intervention for validating output variables.
Some more major difference between Supervised and Unsupervised Machine Learning
Applications: Supervised Learning is perfect for spam detection, weather forecasting, etc. In contrast Unsupervised Machine Learning is an ideal fit for anomaly detection, medical imaging, etc.
Complexity: Supervised Learning is simple and calculated through programs like R and Python. Unsupervised Learning requires powerful tools to work with large chunks of unclassified data. Unsupervised Learning is more complex as compared to supervised Learning as it requires large data sets.
Potholes: Supervised Learning is time consuming and requires expertise. Unsupervised Learning can have extremely inaccurate results without any human supervision.
Goals: The goal of Supervised Learning is to predict outcomes for any new dataset. On the other hand, In Unsupervised Learning, the goal is to collect information from a large amount of new data.
Leverage Ksolves’ expertise to choose the right approach
As we have discussed all the aspects of both the approaches, we can say that both have their own advantages and disadvantages but choosing between Supervised and Unsupervised Machine Learning is challenging and depends on many factors. Ksolves has been in the industry as a leading software development company for many years and helping organizations grow with 350+ experts. We will evaluate your input data to define your goals and discuss the best possible option.
We have worked for both small and large enterprises providing highly accurate and trustworthy results. If you wish to know more about Machine Learning, write to us in the comments below.
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