Feature set
A feature set is a collection of input variables or representations considered or supplied for a model or modeling operation.
Statistics Every Programmer Needs
A feature subset is a selected collection of predictor variables considered for a model operation such as a tree split.
- How it is used
- Defines predictor inputs for training, testing, and splitting decisions in tree-based machine-learning workflows.
- How deep it goes
- Shows column selection, label separation, reproducible splitting, and random feature consideration, but does not derive feature-selection criteria.
- Examples
- Uses football play variables such as quarter, distance, venue, score differential, and play type.
- What you can do after
- Apply it via scikit-learn by separating X and y, partitioning the data, and fitting tree-based classifiers.
Read how Statistics Every Programmer Needs explains feature set →
Graph Algorithms for Data Science
A feature set is a collection of graph relationships retained to calculate network features for predictive modeling.
- How it is used
- Supports link prediction by supplying relationships for calculating network features without reusing supervised samples.
- How deep it goes
- Defines the split and explains leakage, then demonstrates random relationship selection without detailing feature algorithms or model training.
- Examples
- Uses the Twitch shared-audience network and a Neo4j query that retains approximately 90% of relationships as FEATURE_REL edges.
- What you can do after
- Apply it via Neo4j to construct a relationship subset for network-feature calculation in a link-prediction workflow.
Read how Graph Algorithms for Data Science explains feature set →
Related concepts
- configurationconfigured by · A specification of settings, parameters, or component relationships that determines how a system, application, model, or process is structured or operates.
- random forestuses · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
- deep learningused by · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
- gradient boostingused by · Gradient boosting is an ensemble-learning technique that sequentially adds predictive models, typically weak learners, to reduce a loss by fitting its gradient.
- feature selectioncontrasts with · Feature selection is the process of choosing a subset of input features for use in a modeling, analysis, or prediction task.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.