Unsupervised learning

As explained in Statistics Slam Dunk

Unsupervised learning is a machine-learning approach that discovers structure or patterns in data without labeled target values.

Unsupervised learning finds patterns, structures, or relationships in data using input variables alone. Because no target or output values guide the analysis, the question is open-ended: discover useful groupings or relationships rather than predict a known outcome. section 11.0

Use unsupervised learning to:

• Group observations into meaningful clusters, such as customer segments or NBA player and team profiles. section 11.0
• Explore relationships among two or more variables through multivariate analysis. section 11.0
• Develop an analysis around a concrete question even when the desired pattern is not known in advance. section 11.0

Clustering is a central application. Hierarchical clustering can investigate whether top-five draft picks form a distinct career-expectation group, while K-means creates clusters after choosing a number of groups, K. Analysts can compare candidate K values and use data-based methods to select an appropriate count. section 1.3 section 11.0

NBA draft and tanking

An NBA segmentation analysis uses R’s graphical capabilities, expected-value calculations, and an unsupervised learning algorithm to examine whether the data support tanking as a route toward better draft access and superstar talent. section 3.1

Figure 1.5 A typical chapter flow and, not coincidentally, the typical end-to-end flow of most real-world data science and statistics projects

Follow the end-to-end analytical flow from importing and wrangling data through exploration, visualization, testing or modeling, and conclusions. section 1.3

How Statistics Slam Dunk covers it

How deep it goes

Defines the approach and illustrates clustering, but defers detailed algorithm implementation to K-means and hierarchical-clustering analyses.

Examples

Uses NBA draft segmentation, tanking analysis, K-means customer segmentation, and hierarchical clustering of draft-pick career expectations.

What it assumes

Assumes basic data-analysis setup while introducing the distinction between input-only unsupervised and input-output supervised methods.

What you can do after

Apply it via R to construct clusters, compare candidate cluster counts, visualize results, and interpret actionable groupings.

How it connects

Connects unsupervised learning with supervised learning, clustering, multivariate analysis, customer segmentation, and data visualization.

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Related concepts

• supervised learningcontrasts with · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
• K-means clusteringincludes · K-means clustering partitions data into a specified number of groups by assigning observations to representative centers and iteratively updating those centers to reduce within-group distances.
• density estimatorincludes · A density estimator is a statistical method or model that estimates the probability density underlying observed data.
• hierarchical clusteringincludes · Hierarchical clustering is a clustering method that organizes observations into nested groups by successively merging or splitting clusters.
• 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.
• clusteringincludes · Clustering is the technique of grouping data items according to similarity or shared characteristics.

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.