Supervised Learning

TL;DR

• A model learns patterns from labeled examples (inputs paired with correct outputs) to predict labels for new data.
• Requires labeled data, which can be time-consuming and costly to produce.
• Model choice and hyperparameter tuning matter; evaluate performance on a separate test dataset using metrics such as accuracy, precision, and recall.

Definition

Supervised learning is a type of machine learning where a model is trained on labeled data, meaning the data consists of both input features and corresponding correct outputs. The model is then able to make predictions on new, unseen data based on the patterns it has learned from the training data.

Explanation

Supervised learning depends on datasets where each example includes input features and an associated correct output (label). During training the model discovers patterns and relationships in the labeled data so it can assign outputs to new inputs. Key practical aspects include:

• The need for labeled data, which often requires manual labeling by human experts and can be time-consuming and costly.
• Selecting an appropriate model and tuning hyperparameters, since different models and hyperparameter settings can significantly affect predictive accuracy (for example, a decision tree might work well for one task while a random forest might be better for another).
• Evaluation using a separate test dataset that the model has not seen during training, with common metrics for classification tasks including accuracy, precision, and recall.

Examples

Spam filter

Input features might be the words or phrases in an email, and the correct output would be whether or not the email is spam. A model is trained on a dataset of labeled emails (some marked as spam and others not). As the model learns the patterns and characteristics commonly found in spam emails, it can make predictions on new emails, classifying them as spam or not spam.

Credit card fraud detection

Input features could include details about a transaction such as the amount, location, and time of the transaction, with the correct output indicating whether the transaction is fraudulent. A model is trained on a dataset of labeled transactions (some marked as fraudulent and others not). By learning patterns commonly found in fraudulent transactions, the model can identify potential fraud in new transactions.

Notes or pitfalls

• Labeled data is essential but can be costly and time-consuming to obtain because it often requires manual labeling by human experts.
• Choosing the right model and properly tuning hyperparameters is important, as these decisions can have a significant impact on prediction accuracy.
• Always evaluate performance on a separate test dataset to assess how well the model generalizes to unseen data.

Related terms

• Decision tree
• Random forest
• Hyperparameter
• Test dataset
• Accuracy
• Precision
• Recall