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domaincienciadedatos.net
summaryThis content covers various topics in machine learning with Python and Scikit-learn, including linear regression, logistic regression, ridge, lasso, and elastic net regularization. It also discusses decision trees with Python, random forests, grid search of models, out-of-bag error, early stopping, gradient boosting, and support vector machines (SVMs). Additionally, it covers neural networks with Python, partial dependence plots (PDPs) for interpreting predictive models, calibrating machine learning models, and quantile regression. The content also introduces machine learning with H2O in Python and R, algoritmic genetic selection of predictors, cross-validation and one-leave-out validation techniques, non-linear regression methods like polynomial regression, splines, smooth splines, and generalized additive models (GAMs), as well as random forests, gradient boosting, and C5.0 algorithms. The content also covers SVMs, partial dependence plots (PDPs), quantile regression forest distributional regression forest with random forests, gradient boosting quantile regression, and model selection GAMLSS models. It further discusses machine learning with R and Caret, machine learning with H2O in R, machine learning with R and tidymodels, the genetic algorithm for feature selection, and bayesian optimization of hyperparameters. The content also covers association rule mining, recommendation systems, and using DALEX for model explanations.
titleData science, theory and practical examples in R and Python
descriptionTutorials and examples on statistics, algorithms, machine learning, data science, artificial intelligence and programming in R and Python
keywords
upstreams
downstreams paypal.com, github.com, linkedin.com, creativecommons.org, youtube.com
nslookupA 3.72.140.173, A 18.192.231.252
created2024-10-17
updated2024-10-21

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