In general terms, we will consider two approaches to data analysis and processing – supervised learning (with a teacher) and unsupervised learning (without a teacher). The main difference is that the former uses tagged data to aid in forecasting, while the latter does not. But both approaches have more subtle differences and key areas in which they excel.
What is Supervised Learning?
Supervised learning Is an approach to machine learning based on the use of datasets of labeled data. Such datasets are used to create algorithms aimed at classifying data or predicting results accurately. Using labeled inputs and outputs, the model can match inputs and outputs for accuracy and gradually train.
Supervised learning can be divided into two types (according to tasks related to data mining): classification and regression.
- In solving problems classificationfor example, to separate apples from oranges, an algorithm is used to accurately categorize the test data. In the real world, machine learning algorithms can be used to sort spam into a separate email folder. Linear classifiers, support vector machines, decision trees and random forest Are all common classification algorithms.
- Regression – another type of supervised learning method that uses an algorithm to identify the relationship between the dependent and independent variables. Regression data models help predict numerical values based on point data, such as future sales revenue for a particular company. Common regression analysis algorithms include linear regression, logistic regression, and polynomial regression.
What is unsupervised learning?
When unsupervised learning machine learning algorithms are used to analyze and group datasets of unlabeled data. These algorithms reveal patterns in the data without human intervention (therefore, they are “out of control”).
Unsupervised learning models are used to accomplish three main tasks – clustering, association, and dimensionality reduction:
- Clustering Is a data mining technique used to group unlabeled data based on similarities and differences. For example, in K-Means clustering algorithms, similar data points are grouped into groups, where the K value represents the group size and the degree of structuredness. This method is suitable for market segmentation, image compression, etc.
- Association – a method of unsupervised learning, in which certain rules are used to identify the relationships between variables and a given set of data. These methods are often used to analyze shopping behavior and create recommendation services and product selection in the “Buy with this product” categories.
- Dimension reduction Is a learning method that is used when there are too many features (or dimensions) in a given dataset. It reduces the amount of input data to a manageable amount while maintaining its integrity. This technique is often used during data processing, such as when autoencoders remove noise from visual data to improve image quality.
Key Difference Between Supervised and Unsupervised Learning: Labeled Data
The main difference between the two approaches is the use of datasets. Simply put, supervised learning uses labeled inputs and outputs, while unsupervised learning does not.
In supervised learning, the algorithm “learns” by making predictions from the training dataset and adjusting them until the correct answer is obtained. While supervised learning models are generally more accurate than unsupervised learning models, they require direct human intervention and accurate data labeling. For example, a supervised learning model can predict how long it will take to commute to work depending on the time of day, weather conditions, and so on. But first, it needs to be taught so that it understands that rain will increase travel time.
In contrast, unsupervised learning models independently learn the internal structure of unlabeled data. However, they still require little human intervention to validate the output variables. For example, an unsupervised learning model might reveal that online shoppers often buy groups of products at the same time. That said, the data analyst will need to check whether it is advisable for the advisory service to group baby clothes, diapers, applesauce, and sippy cups into one group.
Other key differences between supervised and unsupervised learning
- Objectives. The goal of supervised learning is to predict outcomes from new data. You know in advance what kind of result to expect. The goal of unsupervised learning is to gain useful information from a huge amount of new data. During training, the machine itself determines which information from the set is unusual or of interest.
- Areas of use. Supervised learning models are ideal for spam detection, sentiment analysis, weather forecasting, price changes, and more. Unsupervised learning models are designed to identify variances, improve recommendation services, predict customer behavior, and medical imaging.
- Complexity. Supervised Learning is a simple machine learning technique that is usually calculated using programs like R or Python. Unsupervised learning requires powerful tools to deal with large amounts of unclassified data. Unsupervised learning models are highly computationally complex, since a large training sample is needed to obtain the required results.
- Disadvantages. Unsupervised learning models can be time consuming, and marking up inputs and outputs requires experience and knowledge. Unsupervised learning methods can give very imprecise results if the output variables are not validated by humans.
Supervised versus Unsupervised Learning: Which Is Better?
Classifying big data in supervised learning is not an easy task. However, the results obtained are accurate and reliable. Conversely, unsupervised learning allows you to process large amounts of data in real time. However, in this case, there is a lack of transparency regarding data clustering and there is a higher risk of inaccurate results. The way out of the situation is partially supervised learning.
Partially supervised learning – the golden mean. This method allows you to use a dataset that includes both tagged and untagged data. It is especially useful when you have difficulty extracting relevant features of the data and you are working with a large amount of data.
Partially supervised training is ideal for working with medical imaging: a small amount of training data can significantly improve accuracy. For example, a radiologist might tag a small set of CT scans with tumors or abnormalities so the machine can more accurately identify patients who need more attention.
For more information on developing machine learning models, see the free tutorials on the developer portal IBM Developer Hub…