1. Build a custom cross-validation function for a machine learning model in R, ensuring that each fold contains a representative distribution of the target variable. 2. Create an optimization algorithm in R to find the minimum of a complex, non-convex function with multiple parameters. 3. Given a time series dataset, implement a seasonal decomposition using LOESS (STL) and explain how each component contributes to the overall pattern.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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I need a correct and unique answer without any plagiarism otherwise I will reduce your rating for sure and will report. Don't send a plagiarised response.
1. Build a custom cross-validation function for a
machine learning model in R, ensuring that each fold
contains a representative distribution of the target
variable.
2. Create an optimization algorithm in R to find the
minimum of a complex, non-convex function with
multiple parameters.
3. Given a time series dataset, implement a seasonal
decomposition using LOESS (STL) and explain how
each component contributes to the overall pattern.
Transcribed Image Text:1. Build a custom cross-validation function for a machine learning model in R, ensuring that each fold contains a representative distribution of the target variable. 2. Create an optimization algorithm in R to find the minimum of a complex, non-convex function with multiple parameters. 3. Given a time series dataset, implement a seasonal decomposition using LOESS (STL) and explain how each component contributes to the overall pattern.
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