Supervised Learning Guide for ML Beginners [2024]

Imagine reading through customer reviews and instantly knowing if people are happy, upset, or just neutral about your product. 

Supervised learning can do this automatically by learning from labeled reviews. It picks up on the patterns and words that show if a review is positive, negative, or somewhere in between. This means you can quickly get a sense of how your customers are feeling without reading every single review.

This article will guide you through the basics of supervised learning, the types of SL algorithms, and real-life applications.

What is Supervised Learning and How Does It Work?

Supervised learning is a type of machine learning where input data and corresponding output labels are used to train a model. This means that the model can learn the relationship between inputs and outputs so that it can make accurate predictions on new, unseen data. 

Key Definitions

• Labeled data: A dataset that has been tagged with one or more labels. In the customer reviews example, each review might be labeled as “positive”, “negative”, or “neutral”. These labels will then help a machine learning model learn and make accurate predictions. 
• Training Process: The process whereby the machine learning model learns to recognize patterns and relationships in the labeled data, so it can make accurate predictions on unseen examples. 

Supervised Learning vs. Unsupervised Learning

While supervised learning is like learning with a teacher who provides answers, unsupervised learning is like discovering patterns without any guidance. Here’s a quick table comparing the two machine learning approaches:

What’s Semi-Supervised Learning?

Semi-supervised learning combines elements of both supervised and unsupervised learning by using a mix of labeled and unlabeled data to train models. Essentially, it combines a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy.

Imagine you have a few labeled photos of cats and dogs and many more photos that are not labeled. Semi-supervised learning uses the labeled photos to learn the difference between cats and dogs, then uses the unlabeled photos to improve its understanding. This way, the model gets better at recognizing cats and dogs, even with limited labeled examples.

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Types of Supervised Learning Algorithms

There are two main types of supervised learning algorithms: regression and classification. 

Regression Algorithms

• Linear Regression: Linear regression is a simple statistical method used to understand the relationship between two variables. It predicts the value of a dependent variable (y) based on the value of an independent variable (x) by fitting a straight line to the data points. For example, you can use linear regression to predict someone’s weight based on their height by finding the best-fitting line that shows how weight tends to increase with height. 

• Logistic Regression: Logistic regression is a statistical method used for binary classification, meaning it predicts one of two possible outcomes. Unlike linear regression, which predicts continuous values, logistic regression predicts probabilities that a given input belongs to a particular category. For example, logistic regression can help determine the probability if a student will pass the exam given their study hours. For instance, if a student studies for 5 hours, logistic regression might predict there’s an 80% chance they will pass. Based on this probability, the model classifies the student as likely to pass or fail.

Classification Algorithms

• Naive Bayes: Based on Bayes’ theorem, it assumes that the features of the data are independent of each other, which is often not the case in real life, but it still works well in many situations.
• Decision Trees: Decision trees work by splitting the data into branches based on feature values, creating a tree-like structure of decisions. 
• Random Forest: Random forest combines multiple decision trees to improve accuracy and reduce overfitting (where the model performs well on training data but poorly on unseen data). It works by creating many decision trees during training and then averaging their results (for regression) or using a majority vote (for classification).
• Support Vector Machines (SVM): SVM works by finding the optimal boundary (or hyperplane) that best separates different classes of data. The goal is to maximize the margin between the closest data points of each class, known as support vectors.
• K-Nearest Neighbors (KNN): KNN works by finding the ‘k’ closest data points (neighbors) to a new data point and making predictions based on the majority class or average of these neighbors.
• Neural Networks: Neural networks are a type of machine learning model inspired by the human brain. They consist of layers of interconnected nodes (neurons) that process data and learn patterns. Neural networks are particularly good at handling complex tasks like image and speech recognition.

Applications of Supervised Learning in Real-life

Regression Application: House Price Prediction

Supervised learning helps predict house prices based on factors like location, size, number of bedrooms, and age of the house. By looking at past data with these details, a regression model can guess how much a new house might cost. This is super helpful for buyers and sellers to understand the market value and make better decisions.

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Classification Application: Medical Diagnosis

Supervised learning is used to figure out if a patient has a certain disease based on their medical info, like symptoms and test results. For example, a classification model can learn to diagnose diabetes by looking at labeled patient data and sorting people into those who have diabetes and those who don’t. This helps doctors make accurate diagnoses and provide the right treatment quickly.

Advantages and Disadvantages of Supervised Learning

While supervised learning is highly effective for tasks requiring high accuracy and clear class definitions, it has its limitations related to data labeling, computational demands, and potential overfitting. In the table below, we dive into each of these in detail:

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