nbsword's blog

Best of ml:  https://github.com/ml-tooling/best-of-ml-python EDA make easy : Pandas-Profiling, Sweetviz,  Autoviz, D-Tale Classification metrics Confusion Matrix ROC AUC Gini Coefficient Gain and Lift Charts KS Chart (Kolmogorov-Smirnov) Regression metrics MSE, RMSE MAE MAD RAE (Relative Absolute Error) RSE (Relative Squared Error) R-squared and Adj R-squared Analysis of Residuals Competition Feature Selection: eli5, lofo, Data loading: Faker, Tensorflow Datasets, datasets, Pdfminer.six Imbalanced data: imblearn Parameter Optimization: Optuna, Keras Tuner, skopt(scikit-optimize), Hyperopt AutoML: H2O, NNI (Neural Network Intelligence), auto sklearn, auto keras, TPOP Algo: Lightgbm, Xgboost, Catboost, Lazypredict More effetive pandas: Vaex Model Interpretability: LIME, SHAP, interpret, alibi Missing value imputer: sklearn.impute.IterativeImputer Training ( Workflow & Experiment Tracking): Tensorboard, MLFlow, TensorWatch, Data Version Control(DVC), Metaflow NLP NLP: Kashgari, FastNL

對目前為止的常見影像資料增強方法的 Summary

這篇主要對幾個常見(?的資料降維方法做統整