Supervised and Unsupervised Learning in Machine Learning are two fundamental learning methodologies that are widely known. If you are starting out in Machine Learning, you will see these terms more often than others. In this article, I will elaborate on the critical differences between supervised and unsupervised learning in machine learning, and also what are these techniques and some of their examples in the real world. They both have different evaluation metrics according to the approach on the basis of what the performance is measured. There are different libraries such as Scikit-learn with the help of which you can implement these algorithms and get a quick start.
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What is Supervised Learning?
Supervised learning, one of the fundamental machine learning techniques, involves training algorithms with labeled data. In this scenario, the input data consists of features (also known as predictors) and corresponding known output labels. The primary objective is for the algorithm to learn a mapping function that can accurately predict output labels for new, unseen data.
The model examines examples, identifying patterns and relationships between the inputs and labels. It then adjusts its internal parameters to minimize the difference between its predictions and the actual labels. This iterative process continues until the model’s predictions align closely with the correct labels
Types of Supervised Learning
Supervised learning algorithms can be further categorized into regression and classification.
- Classification: In classification tasks, the model is trained to predict discrete class labels. For instance, classifying emails as spam or not spam, or identifying whether an image contains a cat or a dog. The model learns from past examples and generalizes its knowledge to new, unseen data.
- Regression: Regression tasks involve predicting continuous numerical values. Consider predicting house prices based on features like location, size, and number of bedrooms. Here, the model learns the relationship between the input features and the target variable, allowing it to make predictions for new instances.
In a Nutshell, Regression algorithms are used where a continuous target variable is to be predicted, whereas classification algorithms are used in cases where the prediction is to be in classes.
Supervised Learning Examples: Real-world Applications
Supervised learning finds applications in various domains, such as email spam filtering, medical diagnosis, image recognition, and sentiment analysis. These real-world examples demonstrate how supervised learning algorithms can make accurate predictions and classify data based on labeled training data.
For example, you have a dataset in which Emails are Labeled as Spam and Non-Spam. As you can guess this problem is of Classification Supervised Learning. Now, you will train a classification model, let’s say you took SVM(Support Vector Machines). Now you trained your SVM model on some part of this data and left out the other part of the data as test data so that you can check the performance of your model on the test data. After the training is done, you can evaluate your model on the testing data and check the performance of your model based on how it categorizes spam and non-spam emails.
What is Unsupervised Learning?
Unsupervised learning, unlike supervised learning, deals with unlabeled data. The algorithm explores the underlying structure or patterns within the data without any predetermined labels or output values. Unsupervised Learning identifies inherent relationships, group similar data points, or discover hidden patterns and features.
Unsupervised learning techniques include clustering, dimensionality reduction, and association rule learning. Clustering algorithms, such as K-means and hierarchical clustering, group data points based on their similarity. Dimensionality reduction techniques, like Principal Component Analysis (PCA), simplify complex data by reducing its dimensionality while preserving critical information. Association rule learning uncovers interesting associations and patterns within large datasets.
Unsupervised Learning Examples: Real-world Applications
Unsupervised learning finds applications in various domains, such as customer segmentation, anomaly detection, topic modeling, and recommender systems. These real-world examples highlight how unsupervised learning algorithms can identify patterns and relationships in unlabeled data, leading to valuable insights and improved decision-making.
Key Differences Between Supervised and Unsupervised Learning in Machine Learning
The key differences between supervised and unsupervised learning can be summarized as follows:
Quick Look into what we discussed
- Requires labeled training data with input features and corresponding output labels.
- Aims to predict accurate output labels for new, unseen data.
- Categorized into regression and classification tasks.
- Algorithms include linear regression, logistic regression, decision trees, SVM, and neural networks.
- Examples: Email spam filtering, medical diagnosis, and image recognition.
- Deals with unlabeled data, focusing on discovering patterns, relationships, or structures.
- Does not have predefined output labels.
- Techniques include clustering, dimensionality reduction, and association rule learning.
- Examples: Customer segmentation, anomaly detection, topic modeling, and recommender systems.
Bringing Them Together: Semi-Supervised Learning and More
Well, the boundary between supervised and unsupervised learning isn’t always rigid. There’s a middle ground called semi-supervised learning, where a model is trained on a combination of labeled and unlabeled data. This approach can be especially useful when labeling data is costly or time-consuming.
Moreover, there are other specialized techniques like reinforcement learning, where an agent learns to interact with an environment to maximize rewards and transfer learning, where knowledge gained from one task is applied to another related task. Reinforcement learning is a whole world in itself, we will discuss in upcoming posts about reinforcement learning.