The Difference Between Supervised and Unsupervised Learning

Supervised and Unsupervised Learning are the basics of Machine Learning approaches (James et al., 2013). “The main difference is supervised learning uses labeled data to predict outcomes, while unsupervised learning does not.” (Delua, 2021) However, some nuances exist between them. Therefore, we will discuss this in detail so "you can choose the best method for your particular situation" (Delua, 2021) in this discussion.

What is supervised learning?

Firstly, I want to mention that supervised learning is a machine-learning approach. It needs to use the labeled dataset to classify data and predict outcomes accurately (Delua, 2021). Its model will use supervised algorithms into labeled inputs and outputs to measure accuracy and learn over time (Delua, 2021). Secondly, classification and regression are two problems we face when working with supervised learning:

1. Classification problems use algorithms to classify test data into specific categories. For example, we want to organize spam mail. In that case, we have to use supervised algorithms, such as linear classifiers, support vector machines, decision trees, and random forests in folders in our mail account (Delua, 2021).
2. Regression problems also use supervised algorithms to explore "the relationship between dependent and independent variables.” (Delua, 2021) Popular regression algorithms include linear, logistic, and polynomial regression (Delua, 2021). We will base it on the different data points to predict numerical values (Delua, 2021). For example, if we want to predict a shop's sales revenue, we face a regression problem.

What is unsupervised learning?

In contrast to supervised learning, "unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets” (Delua, 2021) to find hidden patterns without our intervention. Clustering, association, and dimensionality reduction are three main tasks for unsupervised learning:

1. We use cluster algorithms to "group unlabeled data based on their similarities or differences."? (Delua, 2021) For example, suppose we want to market segmentation or image compression; we need to use K-means clustering algorithms to assign similar data points into specific groups (Delua, 2021).
2. Another example is if we want to analyze and recommend what customers should watch movies on Netflix. We should use the association method in unsupervised learning to find the different rules and relationships between variables in a given dataset (Delua, 2021).
3. The last is the dimensionality reduction method in unsupervised learning. We need to use this method in the processing data stage to reduce the number of data inputs when it is too high (Delua, 2021). The main aim is to manage and preserve the data integrity easily. For example, this method can improve image or video quality.

The difference between supervised and unsupervised learning

As mentioned above, the main difference between supervised and unsupervised learning is that “one uses labeled data to help predict outcomes, while the other does not.” (Delua, 2021) However, we can analyze some other differences between us as the following:

1. Goals: Supervised learning aims to predict outcomes for new data based on the labeled dataset (James et al., 2013). Maybe we know our result expectations. In addition, we will get insights from large volumes of new data after applying unsupervised learning.
2. Applications: Spam detection, sentiment analysis, weather forecasting, and pricing predictions are these supervised learning problems. “In contrast, unsupervised learning is an excellent fit for anomaly detection, recommendation engines, customer personas, and medical imaging.” (Delua, 2021)
3. Complexity: Supervised learning is simple (James et al., 2013). In contrast, unsupervised learning is an advanced method and complex because we need to use powerful tools with big data of unlabeled datasets (James et al., 2013).
4. Drawbacks: We need expertise about the problems we are working on. For example, suppose we will design a supervised model for sentiment analysis. We must understand psychology, natural language processing (NLP), etc. In addition, supervised learning requires labeled datasets, so we will spend more time on this stage. "Meanwhile, unsupervised learning methods can have wildly inaccurate results unless we have our intervention to validate the output variables.” (Delua, 2021)

Which is best for us?

It is difficult to say which one is better for us. As we know, both supervised and unsupervised learning are active based on the data. Therefore, to choose suitable approaches for our specific situations, we need to depend on how we assess the structure and volume of our collected data. In my opinion, we need to answer some questions (Delua, 2021) to evaluate and define our dataset:

1. Is it labeled or unlabeled data?
2. Do we have experts that can support additional labeling?
3. What problem do we face? Which fields do we work in?
4. Which algorithms will we use?
5. Are there algorithms with the same dimensionality we need (number of features, attributes, or characteristics)?
6. Can they support our data volume and structure?
7. How accurate should we expect our model to be?

In summary, supervised learning and unsupervised learning are two fundamental methods in Machine Learning. Supervised learning uses labeled data to predict outcomes, making it suitable for tasks like spam detection, sentiment analysis, and weather forecasting. Conversely, unsupervised learning develops models from unlabeled data to discover hidden patterns, fitting tasks like anomaly detection, recommendation systems, and medical image analysis. The complexity of both methods varies, with supervised learning being more straightforward than unsupervised learning, which can be more intricate. However, their choice depends on data type, available resources, and project goals. Ultimately, the selection should be based on thoroughly assessing these factors to align with the specific task.

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References

1. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. New York, NY
2. Delua, J. (2021, Mar 12). Supervised vs. Unsupervised Learning: What’s the Difference? IBM Blog. Retrieved on 2023, Sep 12, from https://www.ibm.com/blog/supervised-vs-unsupervised-learning/#