Convex linear models, Linear Regression, Logistic Regression, and Support Vector Machines, to test convexity and performance by using non-linear endogenous features in time series data.
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Updated
Jun 1, 2021 - HTML
Convex linear models, Linear Regression, Logistic Regression, and Support Vector Machines, to test convexity and performance by using non-linear endogenous features in time series data.
Regression Models - Decision Trees (GBM) and Linear Regression (ElasticNet)
PreTHOD: Prediction of THerapy Outcome in Depression, a research project for the development of clinical prediction models to predict therapy outcome of depression.
Study about salaries in data scientists roles applying the following techniques: QDA, LDA, Naive-Bayes approach, Random Forests, KNN, SVM, (Logistic, Lasso and Ridge) Regression and Elastic Nets. Also, study of the database considering linear, simple and multivariate models.
Lasso + Ridge e redução dimensional usando Mínimos Quadrados Parciais.
This is the website repository for the Stata packages lassopack & pdslasso. Please visit:
Experiments in ML with tidymodels
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