Supervised learning

As explained in Advanced Algorithms and Data Structures

Supervised learning is machine learning in which a model is trained from examples paired with labels or target values.

Supervised learning trains a model from labeled examples: each training entry carries either a class or a target value. Training adjusts model parameters to improve predictions for new inputs. section 12.1.1 section 16.4

The task is determined by the kind of output the model must predict:

• Classification predicts a category, such as an object class. section 12.1.1
• Regression predicts a value, such as an estimated goods price. section 12.1.1
• A model is selected before training; gradient descent can then optimize its parameters. section 16.4

Predicting goods prices

A regression model receives labeled examples containing known goods prices, tunes its parameters during training, and predicts prices for new inputs. section 12.1.1 section 16.4

Figure 16.17 A few approaches used in supervised machine learning. All of them use gradient descent for learning. Note that the models are arbitrarily chosen, and also for the loss functions there are other possible choices. Gradients and loss functions for neural networks are omitted for the sake of brevity. While only supervised learning is shown here, there are also clustering algorithms that leverage gradient descent.

Compare supervised model types, loss functions, and gradient-based learning approaches in one overview. section 16.4

How Advanced Algorithms and Data Structures covers it

How deep it goes

Defines labels, targets, model choice, and generalization, but stops short of detailed training procedures.

Examples

Uses object detection and goods-price estimation as classification and regression examples.

What you can do after

Evaluate/understand how labeled examples support models that predict outputs for previously unseen inputs.

How it connects

Contrasts classification, regression, reinforcement learning, and unsupervised learning, while connecting training with gradient descent.

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Related concepts

• machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
• unsupervised learningcontrasts with · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
• labeled datauses · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
• reinforcement learning with human feedbackused by · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
• classifierincludes · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• linear regressionincludes · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

This explainer was generated by Manning's knowledge graph from the text of Advanced Algorithms and Data Structures. The section links point to the parts of the book it draws on.