Machine Learning

Empirical Dynamic Models for Forecasting

Introduction to EDMs for Forecasting Non-stationary data EDMs are a data-driven solution for uncovering hidden dynamic behavior in natural systems, which are often complex and dynamic (referred to as “non-stationarity” or “non-linearity”).

Linear regression with gradient descent

Introduction linear regression with gradient descent This tutorial is a rough introduction into using gradient descent algorithms to estimate parameters (slope and intercept) for standard linear regressions, as an alternative to ordinary least squares (OLS) regression with a maximum likelihood estimator.

Machine Learning the 'Tidy' Way

Introduction to machine learning with tidymodels Tidymodels provides a clean, organized, and–most importantly–consistent programming syntax for data pre-processing, model specification, model fitting, model evaluation, and prediction. Anatomy of tidymodels: * a meta-package that installs and load the core packages listed below that you need for modeling and machine learning rsamples: * provides infrastructure for efficient data splitting and resampling parsnip: a tidy, unified interface to models that can be used to try a range of models without getting bogged down in the syntactical minutiae of the underlying packages recipes: * tidy interface to data pre-processing tools for feature engineering workflows: * workflows bundle your pre-processing, modeling, and post-processing together tune: * helps you optimize the hyperparameters of your model and pre-processing steps yardstick: * measures the effectiveness of models using performance metrics dials: * contains tools to create and manage values of tuning parameters and is designed to integrate well with the parsnip package broom: * summarizes key information about models in tidy tibble()s First, lets load the tidymodels meta-package: library(tidymodels) ## Registered S3 method overwritten by 'tune': ## method from ## required_pkgs.