Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-Sensitive LearningMachine Learning Mastery, 14 jan 2020 - 463 pagina's Imbalanced classification are those classification tasks where the distribution of examples across the classes is not equal. Cut through the equations, Greek letters, and confusion, and discover the specialized techniques data preparation techniques, learning algorithms, and performance metrics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently develop robust models for your own imbalanced classification projects. |
Inhoudsopgave
III Model Evaluation | 35 |
IV Data Sampling | 103 |
V CostSensitive | 177 |
VI Advanced Algorithms | 244 |
VII Projects | 303 |
VIII Appendix | 431 |
IX Conclusions | 444 |
Veelvoorkomende woorden en zinsdelen
binary classification Brier score calculate calibrated probabilities class distribution class imbalance class label class weighting complete example create cross-validation cv=cv data sampling decision tree define evaluation procedure define model model evaluate model scores evaluate the model evaluate_model(X evaluation procedure cv example is listed Example of defining Example of evaluating Example output F-measure flip_y=0 G-mean grid search hyperparameter imbalanced classification dataset imbalanced classification problems Imbalanced Data imbalanced dataset Imbalanced Learning Learning from Imbalanced load the dataset logistic regression machine learning algorithms majority make_classification(n_samples=10000 mammography matplotlib import pyplot mean ROC AUC mean(scores metric minority class n_clusters_per_class=1 n_features=2 n_redundant=0 n_repeats=3 number of examples numpy import mean outliers oversampling pipeline positive class precision-recall curve predicted probabilities pyplot.show random forest random_state=1 RepeatedStratifiedKFold(n_splits=10 results may vary ROC Curve Running the example scatter plot scikit-learn sklearn.datasets import make_classification sklearn.model_selection import RepeatedStratifiedKFold SMOTE Support Vector Machine techniques threshold Tomek Links training dataset tutorial undersampling XGBoost yhat