Data Science for Beginners: Introduction to Supervised and Unsupervised Learning
If you are a data scientist who is getting started with Machine Learning, then the subject matter experts over at runrex.com point out that you should have a clear understanding of what supervised and unsupervised learning are, and the distinction between the two. It is one of the very first things you should learn as far as Machine Learning is concerned since if you are to get into the model building phase, you will need to have a firm understanding of algorithms like linear regression, logistic regression, clustering, among others, and where each of them falls under, as is covered over at guttulus.com. This article should, therefore, be of great help as it will look to discuss the concepts of supervised and unsupervised learning, giving an introduction to the two that should give you a decent understanding of what they are about.
What is Supervised Learning?
As the name indicates, in supervised learning, you train your model on a labeled dataset, which simply means that you will have both raw input data as well as its results. The computer is, therefore, taught by example as it learns from past data and applies the learning to present data to predict future events. One of the main features of supervised learning, according to the gurus over at runrex.com, is high model perfection. The model, therefore, performs fast since the training time taken is less as we already have the desired results in the dataset. The model can then predict accurate results on unseen data or new data even without knowing a prior target.
Supervised learning is classified into two categories of algorithms:
- Classification Models- Classification models, as explained over at guttulus.com, are used for problems where the output variable can be categorized, such as “Red” or “Black”, “Yes” or “No”, “Disease” or “No disease”, “Pass” or “Fail”, and so forth. These models are used to predict the category of data.
- Regression Models- Regression models are used for problems where the output variable is a real value, such as dollars, a unique number, weight, salary, and so forth. These models are often used to predict numerical values based on previous data observations. Some of the regression algorithms that you may be familiar with include logistic regression, ridge regression, linear regression, and polynomial regression.
Some of the practical applications of supervised learning in real-life as covered over at runrex.com include:
- Spam filtration
- Recommender systems
- Text categorization
- Signature recognition
- Stock price predictions
- Predicting housing prices based on the prevailing market price
- Face detection, among many others
What is Unsupervised Learning?
On the other hand, as revealed in discussions on the same over at guttulus.com, unsupervised learning is the method that trains machines to use data that is neither labeled nor classified, allowing the algorithm to act on that information without any guidance. The main task of unsupervised learning is, therefore, to find patterns in the data as no training data is provided, and as such, the machine is made to learn by itself, as the name suggests. Here, the machine is exposed to large volumes of varying data and allowed to learn from that data to provide previously unknown insights and to identify hidden patterns as mentioned earlier on. This means that unsupervised learning doesn’t come with defined outcomes, with the machine determining what is interesting or different from a given dataset.
From discussions on the same over at runrex.com, unsupervised learning is also classified into two categories:
- Clustering- This is one of the most common unsupervised learning methods, and it involves organizing unlabeled data into similar groups called clusters. A cluster is a collection of similar data items. The main goal of clustering is to find similarities in the data points and group similar data points into a cluster, like say, grouping customers based on purchasing behavior.
- Anomaly detection- This method involves identifying rare items, events, or observations that differ significantly from the majority of the data. The idea behind looking for anomalies or outliers in data is that you will be able to identify something suspicious, which is why anomaly detection is often used in detecting bank fraud and medical errors.
Some of the practical applications of unsupervised learning in real-life include:
- Fraud detection
- Malware detection
- Conducting accurate basket analysis
- Identification of human errors during data entry
- Document clustering, and many others
How do you know when to choose one over the other?
Many factors affect which Machine Learning approach is best for any given task when it comes to manufacturing, according to the subject matter experts over at guttulus.com, particularly since each ML problem is different. If you are wondering which strategy is best for your project, you should consider the following factors:
- The data- You need to evaluate the data and find out if it is labeled or unlabeled, or if there is available expert knowledge to support additional labeling. This will help you decide which approach is best, whether you should go with supervised or unsupervised learning.
- The goal- You should also evaluate what your goal is. Is the problem a defined one or a recurring one? Will the algorithm be expected to predict new problems? Defining the goal of the project will also help you choose.
- Available algorithms- You should also review the algorithms that are available to you and establish which ones best suit the problem based on dimensionality, that is, the number of features, attributes, or characteristics. This will also help you choose since you should pick algorithms that suit the overall volume of data and its structure.
- Successful applications- Finally, you should study the successful application of the algorithm type on similar problems to see which ones work best.
The above discussion only just begins to scratch the surface as far as this topic is concerned and you can uncover more information by checking out the excellent runrex.com and guttulus.com.