/ testing accuracy acknowledgmentsxi adjusted rand index (ari) agglomerative clustering evaluating and comparing example of hierarchical clustering linkage choices principle of algorithm chains and pipelines - building pipelines building pipelines with make_pipeline - grid search preprocessing steps grid-searching for model selection importance of overview of parameter selection with preprocessing pipeline interface using pipelines in grid searches - algorithm parameter algorithms (see also modelsproblem solvingevaluating minimal code to apply to algorithm sample datasets - scaling minmaxscaler - normalizer robustscaler standardscaler - - supervisedclassification decision trees - gradient boosting - -nearest neighbors - kernelized support vector machines - linear svms logistic regression naive bayes - neural networks - random forests - supervisedregression decision trees - gradient boosting - -nearest neighbors lasso - linear regression (ols) - neural networks - random forests - ridge - - unsupervisedclustering agglomerative clustering - - - dbscan - -means - unsupervisedmanifold learning -sne - unsupervisedsignal decomposition non-negative matrix factorization - principal component analysis - alpha parameter in linear models anaconda