Unsupervised learning

As explained in Grokking Machine Learning

A machine-learning approach in which a model discovers structure or patterns from data without target outputs.

Unsupervised learning finds patterns in data without target labels to predict. It can reveal structure in unlabeled data, even when the groups it finds have no known meaning. section 2.3

Grouping without labels

A collection of cat and dog images has no labels, so a model cannot predict which pet a new image shows. It can still group similar images, perhaps separating cats from dogs or grouping them by breed. section 2.3

Common branches use patterns in different ways:

• Clustering groups similar data points, such as emails by size and number of recipients. section 2.3
• Dimensionality reduction simplifies data by describing it with fewer features while losing as little information as possible. section 2.3 section 2.5
• Generative learning creates new data points resembling existing data, such as realistic faces, music, or poetry. section 2.5

Unsupervised methods can also preprocess labeled data before supervised learning. They can simplify a dataset by grouping rows or reducing its features. section 2.3 section 2.5

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 data with a dataset containing images but no labels. section 2.1

How Grokking Machine Learning covers it

How deep it goes

Defines the approach and outlines clustering, dimensionality reduction, and generative learning without detailing their algorithms.

Examples

Uses pet-image grouping, email clusters, housing-feature reduction, and generated faces, music, and poetry.

What you can do after

Understand how unlabeled data can reveal groups, simplify datasets, or support generation of similar data.

How it connects

Connects clustering, dimensionality reduction, generative learning, and supervised learning.

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