Artificial intelligence is on the rise, and advances in machine learning are seeing the light of day. Machine learning enables systems to learn from data and improve their performance over time. You can say that it’s the genius behind artificial intelligence. Advancements in machine learning have led to breakthroughs in various fields, from healthcare to finance to autonomous vehicles and natural language processing. Understanding various machine learning types empowers us to solve diverse real-world problems, from predicting trends to automating tasks. This article will be your introduction to supervised learning, a type of machine learning.
Let’s take a look at what supervised learning is, how it works, and its real-world applications.
What Is Supervised Learning?
Supervised learning is a type of machine learning where the algorithm learns to map input data to specific output labels based on labeled datasets. Consider it like making connections between an object and its label. For example, a picture of a dog can be the object, and therefore, the input data. The word “dog” is its label. In supervised learning, the algorithm would be fed a set of training data of what a “dog” looks like. The goal of the algorithm is to generalize from this training data and make predictions or classifications on new and unseen data.
You can think of it like a test in school. You study the material (training data) and take the test on new unseen questions (input data). To pass the test, you have to answer the new questions correctly (output labels) based on the material you have learned (training data).
How Does It Work?
In supervised learning, AI models are trained using labeled datasets. Labeled data is the data where each example is associated with a known and assigned output or target label. You can think of it as each object having a certain “name” or “category”. Training and applying a supervised learning model take several steps.
#1. Data Collection and Labeling
The first step in supervised learning is to collect a dataset that includes both input features and their corresponding output labels. For example, let’s say we want to build a model that identifies pictures of cats as “cat” or “not cat”. The input would be the pictures of cats, and the output labels would be cat/not cat.
Let’s keep in mind that this is a small example. In real-world model training, the dataset would be huge, consisting of hundreds of labels. Labeling data is a time-consuming and labor-intensive task, depending on the complexity of the dataset.
#2. Splitting Data
After gathering the data, the dataset should be split into two subsets: training data and test data. The training data is, well, used to train the model and it consists of 80% of the whole dataset. The test data is therefore to test and evaluate the model on its performance on unseen data. Think of it again like the school test exam. You can’t use the same questions in the textbook again in the exam. The student would have memorized the question. The same thing goes for the model, you can’t test it with data already used to train it.
#3. Choosing an Algorithm
There are various supervised learning algorithms to choose from. Choosing the right algorithm is essential for the success of supervised learning models. Different algorithms have different goals based on the type of dataset. The choice depends, therefore, on the nature of the problem and the characteristics of the data. Supervised learning algorithms include linear regression, decision trees, support vector machines, and neural networks.
#4. Model Training
In this step, the selected algorithm is fed the training data, in this case, the hundreds of thousands of cat images. During the training process, the model learns the underlying patterns and relationships in the data. It learns from the input features and their associated output labels to create a model that can make predictions accurately. The goal is to minimize the difference between the predicted outputs and the labels in the training data. In short, the machine has to pass the exam with correct answers.
#5. Prediction and Evaluation
After successfully training the model, the model’s performance is assessed using the test data. Common evaluation metrics include accuracy, precision, recall, F1-score, and other metrics Depending on whether it’s a classification or a regression algorithm. The predicted outputs are therefore compared against the true labels of the unseen data.
Types of Supervised Learning Algorithms
We’ve talked about supervised learning algorithms earlier, but what are they? There are various algorithms that are used in supervised learning processes and are generally categorized into classification and regression.
Regression algorithms are used in supervised learning models when there is a relationship between the input variable and the output variable. These algorithms are usually used to predict numerical outcomes. Regression algorithms have a wide range of applications. For example, financial analysts use regression models to predict stock prices or investment returns based on historical market data and economic indicators. Here are the most common regression algorithms in supervised learning.
- Linear Regression:
- Popular for predicting continuous output values. Establishes linear relationship between the input features and the target variable.
- Use Cases: Predicting numerical values.
- Polynomial Regression:
- Captures non-linear relationships by using polynomial terms (e.g., quadratic, cubic) in the regression equation.
- Use Cases: Used when relationships between variables aren’t linear. Temperature modeling, demand forecasting, trajectory prediction.
Classification algorithms are simpler to understand than regression algorithms. They are useful when the output variable can be in a category such as two classes: yes/no, spam/not spam, and cold/not cold. Here are the most common classification algorithms in supervised learning.
- Decision Trees:
- Tree-like models that divide the data into subsets based on features to make decisions.
- Use Cases: Classification tasks like disease diagnosis, credit scoring, and customer segmentation.
- Random Forest:
- Combines multiple decision trees.
- Use Cases: Classification tasks with higher accuracy.
- Logistic Regression:
- Used for binary classification where the output is a probability between 0 and 1.
- Use Cases: Binary classification tasks such as spam email detection (spam/no spam).
- K-Nearest Neighbors (KNN):
- A non-parametric algorithm that categorizes new instances based on their nearest proximity to the labeled instances in the training data. For example, if all neighbors vote “A”, the model guesses “A”.
- Use Cases: Classification based on similarity. Such as image recognition, fraud detection, and healthcare.
- Naive Bayes:
- Based on the Bayes’ theorem for probabilistic classification.
- Use Cases: Text classification, such as sentiment analysis.
Supervised Learning Applications
Let’s see where we can use supervised machine learning models.
- Image Classification
- Speech Recognition
- Fraud Detection
- Medical Diagnosis
- Sentiment Analysis
- Autonomous Vehicles
- Natural Language Processing
- Recommender Systems
- Financial Forecasting
Benefits and Limitations
There are many benefits and limitations to supervised learning.
- Predictive Accuracy: Supervised learning achieves high accuracy when trained on high-quality data.
- Feedback: Since supervised learning uses labeled data, it provides great feedback on the model’s prediction.
- Generalization: Supervised models, when properly trained, have the ability to apply their knowledge effectively to make precise predictions on unfamiliar data, rendering them practical for real-world use.
- Interpretability: Certain supervised learning algorithms, such as linear regression and decision trees, offer models that are interpretable, enabling users to grasp the connections between input attributes and forecasted outcomes.
- Labeled Data Dependency: Labeling data is a time-consuming and costly process. Supervised learning’s dependency on labeled data can put a strain on the model’s training.
- Overfitting: The model may excessively fit the training data, capturing noise rather than the core patterns.
- Bias: Biases in training data can lead to biased predictions, perpetuating unfairness or discrimination.
- Privacy Concerns: The use of labeled data may raise privacy concerns as it can reveal sensitive information.
Is Supervised Learning the Go-To AI Method?
There are many types of machine learning algorithms that sit next to supervised learning such as semi-supervised learning, unsupervised learning, and reinforcement learning. This means that there is more than one way to train a machine-learning model. Supervised learning is a widely used and foundational AI method. But whether it’s the “go-to” approach depends on the specific problem and the availability of labeled data. It is a common starting point, but not always the sole or best choice for every AI task. But that’s the beauty of AI, it’s diverse, solves complex problems, and transforms industries.