problem solving building your own estimators business metrics and initial approach to resources - simple vs complicated cases steps of testing your system tool choice production systems testing tool choice pruning for decision trees pseudorandom number generators pure leafs pymc language python benefits of prepackaged distributions python vs python python( , ) statsmodel package language radial basis function (rbfkernel random forests analyzing building data representation and - vs decision trees vs gradient boosted regression trees parameters predictions with randomization in strengths and weaknesses random_state parameter ranking real numbers recall receiver operating characteristics (roccurves - recommender systems rectified linear unit (relu) rectifying nonlinearity recurrent neural networks (rnns) recursive feature elimination (rfe) regression f_regression linearregression - regression problems boston housing dataset vs classification problems evaluation metrics and scoring examples of goals for -nearest neighbors lasso linear models ridge regression wave dataset illustration regularization regularization regularization rescaling example of - kernel svms resourcesix ridge regression robustness-based clustering roots safari books onlinex samplesdefined scaling - data transformation application effect on supervised learning into larger datasets kinds of purpose of training and test data scatter plots scikit-learn alternate frameworks benefits of bunch objects cancer dataset core code for data and labels in documentation feature_names attribute fit method fit_transform method installing knn object libraries and tools - index