Supervised learning

As explained in Grokking Machine Learning

A machine-learning approach that learns from labeled data to predict labels for previously unseen data.

Supervised learning learns from labeled data and predicts labels for previously unseen data. A label may be numerical, such as weight or price, or categorical, such as dog or cat. section 2.1 section 2.2

The basic workflow is:

• Remember the labeled dataset. section 2.2
• Formulate a model or rule connecting inputs with their labels. section 2.2
• Predict the label of a new, unlabeled data point. section 2.2
• Use regression when the answer is numerical and classification when it is a category or class. section 2.5

Dog or cat?

Give a model images labeled “dog” or “cat.” After it formulates a rule from those examples, provide an unlabeled image; it predicts which category applies. section 2.2

Figure 2.1 Labeled data is data that comes with a tag, or label. That label can be a type or a number. Unlabeled data is data that comes with no tag. The dataset on the left is labeled, and the label is the type of pet (dog/cat). The dataset in the middle is also labeled, and the label is the weight of the pet (in pounds). The dataset on the right is unlabeled.

Compare labeled pet images carrying a type or weight with an unlabeled dataset containing images alone. section 2.1

How Grokking Machine Learning covers it

How deep it goes

Defines the labeled-data workflow and regression-classification split, but stops before specific algorithms or implementation details.

Examples

Uses pet images labeled dog or cat, pet weights, housing prices, spam emails, and fraudulent transactions.

What it assumes

Introduces labeled versus unlabeled data from zero and assumes no specific algorithm, programming language, or implementation detail.

What you can do after

Evaluate and understand how labeled examples support numerical regression and categorical classification predictions.

How it connects

Connects labeled data with regression and classification, and contrasts supervised learning with unsupervised and reinforcement learning.

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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 Grokking Machine Learning. The section links point to the parts of the book it draws on.