What is supervised learning?

What is supervised learning?

Supervised learning , also known as supervised machine learning, is a type of machine learning where an algorithm learns from labeled training data to make predictions or decisions without explicit programming. In simpler terms, it's like a teacher supervising the learning process of a student. Let's break down the key components of supervised learning:

1. Data: In supervised learning, you need a dataset that includes both input data (features) and the corresponding desired output (labels or target values). For example, in a spam email classifier, your dataset might consist of email content (features) and labels indicating whether each email is spam or not.
2. Training: The algorithm is trained on this labeled data to learn the underlying patterns and relationships. It tries to find a mapping between the input features and the output labels.
3. Prediction: Once the model is trained, it can make predictions or classifications on new, unseen data. It generalizes from what it learned during training to make predictions on new, unlabeled data.

Types of supervised learning

There are several types of supervised learning algorithms, each suited for different types of tasks and data. Here are some common types of supervised learning:

• Classification: Classification is the most common type of supervised learning. It involves predicting a categorical label or class for input data. Examples include:
  • Image classification (e.g., identifying objects in images).
  • Email spam detection (categorizing emails as spam or not).
  • Sentiment analysis (classifying text as positive, negative, or neutral).
• Regression: Regression is used when the output variable is continuous and numerical. The goal is to predict a real-valued quantity. Examples include:
  • Predicting house prices based on features like square footage, number of bedrooms, and location.
  • Estimating a person's age based on various demographic factors.

How Supervised Learning Works?

In supervised learning, models are trained using labelled dataset, where the model learns about each type of data. Once the training process is completed, the model is tested on the basis of test data (a subset of the training set), and then it predicts the output.

The following pictures explain this:

Suppose we have a dataset of different types of shapes which includes square, rectangle, triangle, and Polygon. Now the first step is that we need to train the model for each shape.

• If the given shape has four sides, and all the sides are equal, then it will be labelled as a Square.
• If the given shape has three sides, then it will be labelled as a triangle.
• If the given shape has six equal sides then it will be labelled as hexagon.

Now, after training, we test our model using the test set, and the task of the model is to identify the shape.

The machine is already trained on all types of shapes, and when it finds a new shape, it classifies the shape on the bases of a number of sides, and predicts the output.

Applications of Supervised Learning

Supervised learning has a wide range of applications across various domains, including:

1. Image Recognition: In computer vision, supervised learning helps identify objects or patterns within images. For example, it can be used in facial recognition, self-driving cars, and medical image analysis.
2. Natural Language Processing (NLP): In NLP, supervised learning is employed for tasks like sentiment analysis, language translation, and chatbot responses.
3. Recommendation Systems: Companies like Netflix and Amazon use supervised learning to recommend products or content to users based on their past interactions and preferences.
4. Medical Diagnosis: Supervised learning models can aid in diagnosing diseases based on patient data and medical records.
5. Finance: Predicting stock prices, fraud detection, and credit scoring are some of the applications in the financial sector.