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Machine Learning Note

Useful package

Category Encoders:encode categorical variable
Featuretools: be utilized with compose & EvalML
- Featuretools automates the feature engineering process
- EvalML automates model building, includes data checks, and even offers tools for model understanding (Tutorial)
- Compose automates prediction engineering
feature-engine
- Variable transformation, selection, preprocessing
- Imputatoin, Encoding, Discretization, Outlier Handling
- Time series features
imbalance: deal with imbalance data issue

feature-engine
mlxtend: wrapper, greedy algorithm, etc (also include some data visualization tool)

sklearn
xgboost
lightgbm
pyearch: Multivariate Adaptive regression spline
scikit-multilearn: Multi-label classification with focus on label space manipulation
seglearn: Time series and sequence learning using sliding window segmentation
pomegranate: Probabilistic modelling for Python, with an emphasis on hidden Markov models. (GMM, HMM, Naive Bayes and Bayes Classifiers, Markov Chains, Discrete Bayesian Networks, Discrete Markov Networks)

sklearn-deap: Use evolutionary algorithms instead of gridsearch in scikit-learn.
hyperopt: bayesian hyperparameter
scikit-optimize: scikit learn hyperparamter
Optuna: Guidance for Optuna

tslearn: time series preprocessing, feature extraction, classification, regression, clustering
sktime: time series classification, regression, clustering, annotation (also can be used in data that is univariate, multivariate or panel)
HMMLearn: Implementation of hidden markov models
pytorchforecasting: time series forecasting model implemented by pytorch

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