Introduction to Machine Learning with Python

Index

A

• A/B testing, Testing Production Systems
• accuracy, Evaluating the Model, Relation to accuracy
• acknowledgments, From Andreas
• adjusted rand index (ARI), Evaluating clustering with ground truth
• agglomerative clustering
  • evaluating and comparing, Evaluating clustering with ground truth
  • example of, Agglomerative Clustering
  • hierarchical clustering, Hierarchical clustering and dendrograms
  • linkage choices, Agglomerative Clustering
  • principle of, Agglomerative Clustering
• algorithm chains and pipelines, Algorithm Chains and Pipelines-Summary and Outlook
  • building pipelines, Building Pipelines
  • building pipelines with make_pipeline, Convenient Pipeline Creation with make_pipeline-Accessing Attributes in a Pipeline inside GridSearchCV
  • grid search preprocessing steps, Grid-Searching Preprocessing Steps and Model Parameters
  • grid-searching for model selection, Grid-Searching Which Model To Use
  • importance of, Algorithm Chains and Pipelines
  • overview of, Summary and Outlook
  • parameter selection with preprocessing, Parameter Selection with Preprocessing
  • pipeline interface, The General Pipeline Interface
  • using pipelines in grid searches, Using Pipelines in Grid Searches-Using Pipelines in Grid Searches
• algorithm parameter, Estimating complexity in neural networks
• algorithms (see also models; problem solving)
  • evaluating, Generalization, Overfitting, and Underfitting
  • minimal code to apply to algorithm, Summary and Outlook
  • sample datasets, Some Sample Datasets-Some Sample Datasets
  • scaling
    • MinMaxScaler, Preprocessing data for ...