How deeply does Data Without Labels cover supervised learning?

Defines training examples and targets, distinguishes regression from classification, and illustrates models with plots and equations rather than a training workflow.

Supervised learning

As explained in Data Without Labels

Supervised learning is a machine-learning paradigm in which models learn from input data paired with desired outputs to predict outputs for unseen data.

Suppose a telecom operator wants to predict which customers will leave. Its historical records contain customer attributes and a known churn outcome. Supervised learning uses examples like these—inputs paired with desired outputs—to learn a model that predicts outcomes for unseen data. The output to predict is the target variable, or label; the input attributes are independent variables. section 1.5.1

Choose the task by the kind of target:

Regression predicts a continuous value, such as revenue, rainfall, or weight from height. A regression line can model the relationship between inputs and the target. section 1.5.1
Classification predicts a category, such as whether a transaction is fraudulent or a customer has churned. Logistic regression is one classification approach; its sigmoid function produces probability scores between 0 and 1. section 1.5.1

Table 1.3 Example of a structured dataset for a telecom operator showing multiple data attributes

ID	Revenue ($)	Duration of service (years)	Avg. cost	Monthly usage (days)	Churned (Y/N)
1001	100	1.1	0.10	10	Y
1002	200	4.1	0.09	25	N
1003	300	5.2	0.05	28	N
1004	200	0.9	0.25	11	Y
1005	100	0.5	0.45	12	Y

In this telecom dataset, revenue, service duration, average cost, and monthly usage are input attributes. “Churned (Y/N)” is the known target in each training example and the outcome to predict for another customer. section 1.5.1

The quality of a prediction depends on what the model learns from labeled historical data. If only some records have labels, semisupervised learning offers a different approach: train first on the labeled records, use the resulting model to assign provisional labels to more records, then combine them for further analysis. section 8.3.1 section 1.5.3

Supervision in a neural network

For face identification, training examples pair face images with people’s names. A neural network learns identifying attributes from those pairs and uses them to distinguish faces. This is supervised training because the desired name accompanies each training image. section 8.2.1

How Data Without Labels covers it

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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 Data Without Labels. The section links point to the parts of the book it draws on.