Quais são as principais diferenças entre aprendizagem supervisionada e não supervisionada?

Back in School, there used to be books that used to be our reference material & had some exercises after each chapter. Those exercises were usually derived from the chapters. You knew the label i.e. you knew which question belonged to which chapter. This is a prime example of supervised learning. Another example could be your childhood, your parents might have taught you the difference between a cat and a dog, they labeled the pictures for you, and your mind found some differences and now your mind could easily differentiate between a cat and a dog/ Supervised learning is a type of learning where we give labels in the dataset and expect the technique to find the reason on the basis of which different classes have been differentiated.

Supervised learning involves training a model on labeled data, where each input comes with an associated output (label). This approach helps the algorithm learn the relationship between the inputs and outputs, making it capable of predicting outcomes for new, unseen data. Supervised learning is typically used for classification tasks, where the objective is to assign inputs to predefined categories, and for regression tasks, where the goal is to predict continuous values.

In supervised learning, the algorithm is trained using a dataset that includes both the inputs and the correct outputs. These outputs, called labels, help the model learn the relationship between the data and the desired result. This approach is commonly used in classification, where the goal is to assign data to specific categories, and in regression, where the objective is to predict a continuous value.

Supervised learning is like having a teacher show you examples with answers. You are given labeled data, where the computer learns the relationship between inputs and outputs. It is like learning to recognize patterns based on examples you have seen before.

Back in School, there used to be books that used to be our reference material & had some exercises after each chapter. Those exercises were usually derived from the chapters. You knew the label i.e. you knew which question belonged to which chapter. This is a prime example of supervised learning. But when it came to exams, there used to be cases when the questions used some sort of concept taught in the book, but the same question was not in the book, it was a test to test your learning, and you were not aware which question belonged to which chapter. This is a prime example of unsupervised learning, here models are given data and they have to find the differences as well as classify them into different classes, no labels are provided.

Unsupervised learning is a type of machine learning where the model is trained on data without labeled responses. It aims to identify patterns and structures within the data. Common techniques include clustering (e.g., K-means, hierarchical clustering) and dimensionality reduction (e.g., PCA, t-SNE). Unsupervised learning is used for tasks like customer segmentation, anomaly detection, and data compression, allowing the discovery of hidden patterns and insights without predefined categories.

Unsupervised learning deals with unlabeled data, meaning the algorithm tries to find hidden patterns or intrinsic structures within the data without guidance on what the outputs should be. Common applications include clustering, where data points are grouped based on similarity, and dimensionality reduction, which simplifies data while preserving important information. This type of learning is essential for exploratory data analysis and finding insights in large datasets without prior knowledge of outcomes.

When we talk about unsupervised learning, it's like navigating without directions. Here, the data doesn't come labeled, so the algorithm has to figure out patterns independently. It's great for tasks like grouping similar data points (clustering) or simplifying complex data (dimensionality reduction) without guidance.

Supervised learning models include decision trees, support vector machines, and neural networks, which are trained using labeled data to perform specific tasks. In contrast, unsupervised learning models, such as k-means clustering, hierarchical clustering, and principal component analysis (PCA), do not rely on labeled data and instead identify patterns and structures directly from the input data.

In supervised learning, models are trained using labeled data to predict new data labels accurately. Typical models include linear regression for numerical predictions and support vector machines for classification. On the other hand, unsupervised learning, like k-means clustering and principal component analysis, identifies patterns in data without predefined labels.

Supervised learning requires a substantial amount of labeled data to accurately learn the relationship between inputs and outputs, which can be time-consuming and costly to obtain. Unsupervised learning, on the other hand, can work with large volumes of unlabeled data, making it more scalable in situations where labeling data is impractical or impossible.

When we talk about data requirements, supervised learning, and unsupervised learning have distinct needs. Supervised learning relies on labeled data, which can take time and money to gather. On the other hand, unsupervised learning doesn't need labels but often demands more data to uncover patterns and relationships, especially with intricate datasets.

Supervised learning is often more straightforward to evaluate and interpret because the performance can be directly measured against known labels. It is widely used in applications like spam detection, image recognition, and medical diagnosis. Unsupervised learning is more complex and harder to evaluate since there are no predefined labels, but it excels in tasks such as customer segmentation, anomaly detection, and market basket analysis where discovering hidden patterns is crucial.

Supervised learning is simpler because it has clear goals set by the labels. It's great for tasks where prediction accuracy is crucial. Unsupervised learning is more intricate as it explores data without clear labels. It's handy when you're uncertain about what you're seeking or when you want to uncover unexpected patterns.

Choosing an algorithm depends on the nature of the problem and the type of data available. For supervised learning, algorithms like logistic regression, random forests, and gradient boosting are popular. For unsupervised learning, algorithms like k-means clustering, DBSCAN, and t-SNE are commonly used. The choice also depends on factors such as computational efficiency, scalability, and the interpretability of results.

The core ingredient for solving any business problem is understanding the context and forming a problem statement that captures this essence. Once this is clear, the path to algorithm selection becomes clear. Supervised learnings have a target label to predict and use a loss function to minimise the error. On the other hand, unsupervised learning helps in finding patterns and similarities from the underlying data, detecting outliers and even dimension reduction.