What is supervised learning?
Supervised learning is a type of machine learning paradigm in which an algorithm learns to map input data to desired output labels based on a given dataset. In supervised learning, the algorithm is "supervised" or guided by a set of labeled examples, where each example consists of an input (also called a feature or data point) and a corresponding desired output (also called a label or target).
The main goal of supervised learning is to learn a mapping function from the input data to the output labels so that the algorithm can make accurate predictions or classifications on new, unseen data. The process typically involves the following steps:
Data Collection: Gathering a labeled dataset that includes pairs of input data and their corresponding output labels. For example, in a spam email classification task, the input data might be the content of an email, and the output label could be whether the email is spam or not.
Data Preprocessing: Cleaning, transforming, and preparing the data for the learning algorithm. This might involve tasks such as feature extraction, normalization, and dealing with missing values.
Model Selection: Choosing a suitable machine learning model or algorithm that is appropriate for the specific task. The choice of the model can vary based on factors like the nature of the data and the complexity of the problem.
Model Training: Using the labeled dataset to train the chosen model. During training, the model adjusts its internal parameters based on the input-output pairs to minimize the difference between its predictions and the actual labels.
Model Evaluation: Assessing the performance of the trained model on a separate set of data that it hasn't seen before, known as the validation or test set. Common evaluation metrics include accuracy, precision, recall, F1-score, and more, depending on the nature of the problem.
Hyperparameter Tuning: Fine-tuning the hyperparameters of the model to improve its performance on the validation or test set. Hyperparameters are settings that are not learned during training but affect the behavior of the learning algorithm.
Prediction/Inference: Once the model is trained and evaluated, it can be used to make predictions or classifications on new, unseen data.
Supervised learning can be further categorized into two main types:
Classification: In classification tasks, the goal is to predict a discrete class label for each input data point. Examples include spam detection, image classification, and sentiment analysis.
Regression: In regression tasks, the goal is to predict a continuous numerical value for each input data point. Examples include predicting house prices, stock prices, or temperature.
Overall, supervised learning is a fundamental approach in machine learning and has numerous real-world applications across various domains.