‘Hey Siri, what is supervised learning?’
A seemingly simple voice recognition feature like Siri functions by converting audio into textual form which is sent to Apple’s servers to analyze user intent and reflect answers in seconds! This is an application of Machine Learning (ML), which uses different approaches to help systems learn faster. Supervised learning is one such approach that uses labeled data to train machines. Let’s look at this in detail!
How Does Supervised Learning Work?
As the name suggests, supervised learning is where a machine learns but under supervision. It uses labeled datasets to train algorithms how to analyze data and predict accurate outcomes.
In the training phase, datasets are fed into the system to help algorithms learn which output can be mapped to a specific input value. Then, the system is presented with unlabeled test data to check if the algorithm can make accurate predictions. The accuracy is measured through loss function, based on which adjustments are made accordingly. This training phase continues until errors are successfully minimized.
Types of Supervised Learning
There are two types of supervised learning methods while data mining. Let’s take a look at what these are and how you can identify which one to use.
This technique uses a separate algorithm to segregate test data into specific categories, based on the labeled training data set. Classification is generally used when the input data can be tagged or classified into categories. Some examples are, true or false, male or female, etc.
This is used to determine the relationship between dependent and independent variables and draw projections. A few examples are predicting the price of a house, projecting sales revenue within a given time frame, etc.
Supervised Learning Examples
Some common real-life examples are:
- Customer Analysis – Provides better recommendations by identifying patterns in customers’ purchase history
- Predictive Analysis – Offers accurate insights into business data points
- Fraud Detection – Identifies fraudulent transactions by evaluating datasets
- Image Classification – Identifies, locates, and categorizes objects from videos and uses them during vision analysis to accurately identify images
Supervised Learning Algorithms
There are multiple types of supervised learning algorithms that work best when there is enough data available for them to analyze. Complexity of the function, bias within the algorithm and data accuracy are some things to consider before choosing an algorithm. The most popular ones are:
- Linear Regression
- Logistic Regression
- Random Forest
- Naive Bayes
- Support Vector Machine (SVM)
- Decision Trees
- Neural Networks
The Difference Between Supervised Learning and Unsupervised Learning
Since we have already established what is supervised learning, let’s understand unsupervised learning. Unsupervised learning is when the algorithm analyzes data without instructions from humans, as there is no correct output value. Here are some basic differences between the two:
- Data: Supervised learning uses labeled data whereas unsupervised learning uses unlabeled data to determine the outcome
- Complexity and Accuracy: Supervised learning is relatively simple and provides a highly accurate outcome. Unsupervised learning is computationally complex as it requires a larger training set to draw insights
- Applications: Supervised learning is generally used for data projections, fraud detection and sentiment analysis, among other things. Unsupervised learning is a good fit for anomaly detection, medical imaging, Natural Language Processing (NLP), etc.
How Can Supervised Learning Help Build a Work Culture That Emphasizes Learning
The scope of supervised learning is not just limited to single-handedly performing essential business functions. It is also a great tool to enhance organizational culture by encouraging companies to expand growth and automate decision processes.
But how can this create value in a company and promote learning at work? Here are some concrete examples:
- Since it can assess large sets of data and draw insights in a scalable manner, it frees up time for employees to innovate and gives them the opportunity to focus on other areas of work
- It focuses on creating a personalized learning experience for every employee based on their responsibilities
- It empowers employees to take calculated actions that are aligned with the insights drawn from supervised learning when faced with familiar challenges
Challenges of Supervised Learning
We now have a detailed understanding of what is supervised learning and the role it plays in solving real-world computation problems. But there are also some limitations when it comes to implementing sustainable learning systems. Let’s take a look.
- It requires a huge amount of accurately labeled data, which may not always be available
- Training data takes high computation time that may test the machine’s efficiency
- It cannot classify data on its own and requires human intervention, unlike unsupervised learning
- It has limited capabilities and cannot handle certain complex ML tasks
This kind of learning is a great tool for producing data output based on past experience and drawing highly accurate and trustworthy insights. And while it finds applications across a variety of domains and industries, it is important to choose the right machine-learning approach that is best suited for assessing the structure and volume of your data.
These learning systems are a valuable solution for minimizing manual work and making useful predictions based on labeled data. However, it requires immense expertise to develop, scale, and update these systems. If a career in this field entices you and you want to get a deeper insight into what is supervised learning, enroll for an AI and MI course on Emeritus to upskill and build expertise!
By Neha Menon
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