How Does Supervised Learning Work?
Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset. In this context, 'labeled' means that each training example is paired with an output label. The goal of supervised learning is to learn a mapping from inputs to outputs so that the model can accurately predict labels for new, unseen data.
Key Steps in Supervised Learning
- Data Collection: Gather a large dataset that contains both input features and corresponding output labels. For example, if predicting house prices, features could include size, location, and condition, while the label would be the price.
- Data Preprocessing: Clean and preprocess the data to handle missing values, encode categorical variables, and normalize numerical values. This step ensures the data is in a suitable format for training.
- Model Selection: Choose an appropriate algorithm to learn the mapping. Common algorithms include linear regression, decision trees, and support vector machines.
- Training the Model: Use the labeled dataset to train the model by adjusting its parameters so it minimizes the difference between predicted and actual labels.
- Validation: Assess the model's performance using a separate validation dataset. Metrics such as accuracy, precision, and recall are used to evaluate effectiveness.
- Prediction: Once validated, the model can make predictions on new, unlabeled data, applying the learned mapping from inputs to outputs.
Supervised learning is widely used in applications like spam detection, image classification, and medical diagnosis due to its effectiveness in tasks where labeled data is available.