Supervised Machine Learning Outline: Types, Examples, & More

In modern days, we have been adaptive and welcoming in the integration of various tech and systems to make things easier on our part, like Artificial Intelligence, also known as AI. As its application and use have become more normalized, there is a discussion about its beneficial and destructive usage to our environment. However, AI is a broad term that consists of many fields and is actually beneficial and is now being implemented and utilized in our daily lives, but not many are aware of it, such as Machine Learning. A subfield of AI that allows the system to learn and identify patterns from the data and continuously improve its performance over time as it feeds on more data without having been explicitly programmed to do so.

As machine learning has many applications and functions for our current system and program usage, Machine Learning does come with various types, and one of them is the Supervised Learning or Supervised Machine Learning, which this article will focus on exploring and shedding some light. Furthermore, as readers get some insights and understanding of the application and functions of machine learning, specifically the Supervised Machine Learning, this article will also feature the pros and cons of supervised machine learning alongside a side-by-side comparison to another machine learning type, which is the Unsupervised Machine Learning.

Part 1. What is Supervised Learning?

Supervised Machine Learning, a subfield of Machine Learning, where a model, before being put out into a system or program, will use a technique that the model learns from labelled data. Take it as every input data has its corresponding correct output. The model of Supervised Machine Learning identifies patterns, makes predictions, adjusts, and improves itself to reduce errors.

Part 2. How Supervised Learning Works

Taking the explanation of how Supervised Machine Learning works by further listing a few key steps, it follows that it functions, works on unseen data, creates accurate predictions, improves itself over time, and more.

1. Gathering and Collecting Labelled Data

Collect a certain dataset where each input has its corresponding label.

Example: Images of Animals with their names as labels.

2. Segmentation of Datasets

After gathering and collecting labelled data, the next step is to divide the data into two categories, where at least about 80% of the data will be used for training the model, and the other 20% of the data will be used for testing the model.

Example: 80 images of animals with labels will be used as training data, and the remaining 20 images of animals with labels will be kept separate as testing data so that the model will not memorize the answer.

3. Model Training

Begin feeding the training data to the preferred Supervised Learning Algorithm. After that, the model will begin analyzing and finding patterns to map out the correct output—this process is a core concept in machine learning.

Example: Choose an appropriate supervised machine learning algorithm Decision Tree, Support Vector Machine, or Neural Network. In the process, the model will analyze the training data images by learning various patterns, such as shapes, size, lines, pixels, etc.

4. Validate Result and Model Testing

After model training, proceed to use the testing data to evaluate and validate the model to determine how it will perform when going through a new set of unseen data.

5. Deployment

After countless tweaks, testing, and training, once the model becomes stable and has been performing well, it is now time to move into the next phase, and that is the actual deployment of the model into a real system.

Part 3. Types of Supervised Learning

Supervised Machine Learning has two main types of problems it addresses—Classification and Regression—both of which fall under broader types of AI.

1. Classification

A type of supervised learning, such as classification, analyzes data and predicts output of discrete categories, such as classification between Yes or No, Spam or Non Spam Emails, Positive or Negative Diagnosis, etc.

2. Regression

A type of supervised learning, it focuses more on analyzing and predicting output of a continuous or unending variable or values, such as predicting stock exchange prices, house prices, etc.

Part 4. Supervised Learning Algorithms

Linear Regression

Linear Regression is a type of Supervised Algorithm, which is considered the simplest and most used algorithm, mainly used for predicting continuous output values rather than categorization of data. By taking a set of data points, it can find the best data point perfect for predicting and forecasting values of a certain range.

Decision Tree

A popular supervised learning algorithm that can handle complex data. Following a tree-like structure, it branches out until it reaches an end node, where each leaf in the tree represents a possible outcome, making it perfect for both predictive modelling and classification of tasks.

Gradient Boosting

Gradient Boosting is a type of supervised learning algorithm that combines all weak learners or predictors to form a more powerful predictor, correcting the errors made previously. This supervised machine learning algorithm is used when dealing with loads of data.

Naive Bayes Algorithm

Based on Bayes Theorem that operates on conditional probabilities, which handles multi-classification of tasks and predictive modelling for binary classification. This supervised machine learning algorithm is perfect for handling complex tasks and large datasets.

Logistic Regression

Logistic Regression is used for predicting a binary output value or a simple binary classification task, and is commonly used to classify or determine if an input belongs to a certain class by estimating the probabilities using a logistic function. Moreover, as Logistic Regression leans towards predicting the probability of an input in practice, it uses two groups to sort the input into the primary class and the non-primary class.

Random Forest

Random Forests are made up of numerous decision trees that work together to make predictions. As it uses multiple decision trees, the RandomForest's multiple decision trees were all individually trained using various random training datasets, each of which comes with different and independent predictions, which makes it easy to produce the most accurate tallies of data, as it has multiple Decision Tree Algorithms.

Support Vector Machine (SVM)

Support Vector Machine creates a hyperplane, a boundary that separates two sets of data, and it mainly functions as a predictive modelling and classification algorithm. The SVM Algorithm aims to find the best decision boundary by maximizing the hyperplane set on the data, and it will look for the gap between the classes.

K-Nearest Neighbors (KNN)

K-Nearest Neighbors is a type of supervised machine learning algorithm that bases its prediction on the proximity of data. This will classify data points by looking at their nearest neighbour on the graph, which makes it a perfect algorithm for dealing with classification needs.

Part 5. Pros and Cons of Supervised Learning

Part 6. Supervised VS Unsupervised Learning

Part 7. FAQs about Supervised Machine Learning

What are real-world applications of Supervised Learning?

Some of the real-world applications of supervised machine learning are email spam detection, sales pricing forecasting, fraud or scam transaction detection in banking, image recognition, medical diagnosis, and more.

What is labelled data in supervised machine learning?

Labelled data is the data that feeds into the Supervised machine learning model that features input with correct output labels.

Why supervised learning matters?

It matters because it turns data into a working prediction, allowing various fields to make use of decisions based on patterns it learned from the data. It helps in powering, if not all, but most real-world AI-integrated systems and programs, supports better business predictions, and more.

Conclusion

In conclusion, Supervised Machine Learning is a practical and impactful subfield of AI that is almost present in our daily lives. This article not only gives readers a full view of what is Supervised Machine Learning, but also explores its types, gives a brief and concise explanation of how it works, and lists the supervised learning algorithms with descriptions. With a deeper look at the branch of AI machine learning, it is truly among the good applications of AI. Thus, developing one may not be an easy task as it requires knowledge in coding and programming, which one can not learn just overnight.