data pointsdefined data representation - (see also feature extraction/feature engineeringtext dataautomatic feature selection - binning and - categorical features - effect on model performance integer features model complexity vs dataset size overview of table analogy in training vs test sets understanding your data univariate nonlinear transformations - data transformations (see also preprocessingdata-driven research dbscan evaluating and comparing - parameters principle of returned cluster assignments strengths and weaknesses decision boundaries decision function decision trees analyzing building controlling complexity of data representation and - feature importance in if/else structure of parameters vs random forests strengths and weaknesses decision_function deep learning (see neural networksdendrograms dense regions dimensionality reduction discrete features discretization - distributed computing document clustering documentsdefined dual_coef_ attribute eigenfaces embarrassingly parallel encoding ensembles defined gradient boosted regression trees - random forests - enthought canopy estimators estimator_ attribute of rfecv evaluation metrics and scoring for binary classification - for multiclass classification - metric selection model selection and regression metrics testing production systems exp function expert knowledge - ( )= formula facial recognition factor analysis (fa) false positive rate (fpr) false positive/false negative errors feature extraction/feature engineering - (see also data representationtext dataaugmenting data with automatic feature selection - categorical features - continuous vs discrete features defined interaction features - with non-negative matrix factorization overview of polynomial features - with principal component analysis univariate nonlinear transformations - using expert knowledge - feature importance featuresdefined feature_names attribute feed-forward neural networks fit method fit_transform method floating-point numbers index