Stacking
Stacking, or stacked generalization, is an ensemble learning technique that trains a meta-model to combine the predictions of several diverse base models, learning how to best weigh or blend their outputs rather than using a fixed rule like averaging or voting.
7 resources across 2 libraries
Glossary Terms(4)
Bootstrap Aggregating
Bootstrap aggregating, commonly called bagging, is an ensemble learning technique that trains multiple instances of a model on different bootstrap-sampled subs…
Bagging
Bagging, short for bootstrap aggregating, is an ensemble learning method that trains multiple copies of a model on different randomly resampled (bootstrapped)…
Boosting
Boosting is an ensemble learning technique that builds a sequence of models, where each new model is trained to correct the errors made by the combined ensembl…
Stacking (ML)
Stacking, or stacked generalization, is an ensemble learning technique that trains a meta-model to combine the predictions of several diverse base models, lear…
Study Notes(3)
Array Reshaping and Stacking
Reshaping changes an array's shape without altering its data, while stacking and splitting functions combine or divide arrays along chosen axes.
Concatenating DataFrames
Using pandas' concat function to stack DataFrames or Series along rows or columns, and how it differs from merge in purpose and alignment behavior.
MultiIndex Basics
Learn how pandas' hierarchical MultiIndex lets you represent and query higher-dimensional data using multiple index levels on a single axis.