When training an AI model, three primary learning methods stand out: supervised learning, unsupervised learning, and reinforcement learning. In this article, I’ll give you a comprehensive guide to supervised learning, including its definition, mechanisms, various types, and its advantages and disadvantages.
What is supervised learning?
Supervised Learning is one of the learning methods in AI where labeled data is used to train AI models. In this approach, we provide the AI model with a dataset that includes both input data (features) and their corresponding correct answers(labels), and the AI model learns from this dataset, enabling it to predict the correct answers(labels) when given new data(features).
“This is like teaching a computer to recognize patterns and make predictions by showing it lots of examples with answers. Imagine you’re teaching a child to identify different animals by showing them pictures and telling them the names of each animal. The child learns from these examples, and eventually, they can look at new pictures and correctly say what animal is in the picture”
Typically, the labels are referred to as training labels, and the input data(features) within the dataset as training inputs. Each feature-label pair is referred to as an example. Both deep learning and machine learning models can be trained using this learning method.
How does supervised learning work?
First, we collect a dataset(a large set of examples)dataset with known features. From this dataset, select a random subset and obtain the ground truth labels for each data point.
Sometimes, these labels may be present in the collected dataset (e.g., house prices), or may require human annotators to label the data (e.g., categorizing images).
The training set consists of input data (features) and their corresponding labels. The input data represents the problem’s attributes, while the labels represent the desired outcomes that the AI model should learn.
Next, We input the training dataset into the supervised AI model,
During the training phase, the AI model tries to identify patterns, relationships, and associations between the input data/features and the target labels. The ultimate goal is to build a model capable of making precise predictions or classifications when presented with new, unseen data.
Finally, we can feed previously unseen inputs to the learned model, using its outputs as predictions of the corresponding label.
Types of supervised learning
Supervised learning is classified into two categories of algorithms: Regression and Classification.
Regression
When numerical values are used as labels in supervised learning algorithms, we refer to this as a regression problem. Here, the AI model tries to find a functional relationship between independent variables and a dependent variable, to predict continuous numerical values like prices, temperatures, or other quantitative measurements based on input data or features.
For example, in the case of predicting house prices, we use features like size, number of bedrooms, and location as input data for our AI model. The model’s objective is to predict the training label, which, in this case, is the numerical value representing house prices, based on these feature inputs.
Here are some of the popular regression algorithms:
- Linear Regression
- Regression Trees
- Non-Linear Regression
- Bayesian Linear Regression
Classification
In classification, the AI model aims to predict a predefined categorical output variable or class label based on input data or features. Here, the AI model examines features, such as pixel values in an image, and predicts the category (or class) from a discrete set of options.
The simplest form of classification is binary classification, where there are only two classes, like a dataset of animal images with labels such as ‘cat’ or ‘dog’
For instance, let’s consider an AI model tasked with categorizing images of dogs and cats. This model analyzes features such as image corners and animal characteristics and then assigns a probability to each possible class. Imagine that the classifier might see an image and output a probability of 0.9 for the class ‘cat.’ This means that the classifier is 90% confident that the image depicts a cat.
When we have more than two possible classes, we call it multiclass classification, such as recognizing handwritten characters, where the classes could include numbers (0, 1, 2, … 9) and letters (a, b, c, …).
Here are some of the popular classification algorithms:
- Random Forest
- Decision Trees
- Logistic Regression
- Support vector Machines
Advantages of supervised learning
Supervised learning is widely used in various AI models due to its numerous advantages. Here are some of the main advantages:
- Supervised learning systems make decisions that humans can easily comprehend, as they are based on human input.
- Users can determine the number of categories in the training data, providing them with control over the learning process.
- Supervised learning offers a clear understanding of object classes or categories.
- Labeled training data helps models to learn patterns and relationships between inputs and outputs accurately.
- Supervised learning powers personalized recommendation systems, ensuring users receive content or products tailored to their preferences.
Disadvantages of supervised learning
Supervised learning, while powerful and widely used, has its limitations and disadvantages. Here are some of the key drawbacks of supervised learning:
- Acquiring high-quality labeled data for supervised learning can be time-consuming, expensive, and challenging.
- In supervised learning, we need enough knowledge about the classes of objects.
- When there are biases in the training data, it can lead to unfair predictions and decisions.
- The model can only predict data similar to what it was trained on. It can’t handle new or unfamiliar data categories or patterns. This issue can be addressed with unsupervised learning methods.
- The real world is constantly changing, so keeping the supervised learning models up to date to adapt to evolving patterns and trends can be challenging. Therefore, we use the reinforcement learning method for real-world issues.
Applications of supervised learning
Supervised learning has a wide range of applications across various domains because many important tasks can be defined as estimating the probability of something unknown based on available data. Here are some popular applications of supervised learning:
- Image Classification: Supervised learning algorithms are primarily used to identify objects or patterns within images, including tasks like facial recognition, object identification in photos, and medical image analysis.
- Predictive Analytics: Supervised algorithms are used for forecasting sales, revenue, and future product or service demand based on historical data and market variables.
- Recommendation Systems: They recommend articles, movies, or products based on a user’s preferences and behavior, and they target customers with personalized recommendations and offers.
- Medical Diagnosis: Diagnosing diseases and medical conditions based on patient data, such as medical images, patient records, and genetic data.
Supervised learning is a powerful and widely used approach in the AI field, allowing models to predict and classify using labeled data. It’s versatile, It has many uses, including image classification and medical diagnosis. However, it’s important to be aware of its limitations, such as the need for high-quality labeled data and the challenges in adapting to changing real-world conditions. Still, it is an essential tool for tackling complex problems across different fields.