forge dataset frameworks free string data freeform text data high-dimensional datasets histograms hit rate hold-out sets human involvement/oversight gamma parameter gaussian kernels of svc gaussiannb generalization building models for defined examples of get_dummies function get_support method of feature selection gradient boosted regression trees for feature selection - learning_rate parameter parameters vs random forests strengths and weaknesses training set accuracy graphviz module grid search accessing pipeline attributes alternate strategies for avoiding overfitting model selection with nested cross-validation parallelizing with cross-validation pipeline preprocessing searching non-grid spaces simple example of tuning parameters with using pipelines in - with cross-validation - gridsearchcv best_estimator_ attribute best_params_ attribute best_score_ attribute handcoded rulesdisadvantages of heat maps hidden layers hidden units hierarchical clustering high recall index imbalanced datasets independent component analysis (ica) inference information leakage information retrieval (ir) integer features "intelligentapplications interactions - intercept_ attribute iris classification application data inspection dataset for goals for -nearest neighbors making predictions model evaluation multiclass problem overview of training and testing data iterative feature selection jupyter notebook -fold cross-validation -means clustering applying with scikit-learn vs classification cluster centers complex datasets evaluating and comparing example of failures of strengths and weaknesses vector quantization with -nearest neighbors ( -nnanalyzing kneighborsclassifier analyzing kneighborsregressor building classification -