Anabolic Steroids: Uses, Abuse, And Side Effects


1. What this book offers



Section What it contains Why it matters


Table‑of‑Contents (TOC) A clickable list of all chapters and sub‑chapters. The quickest way to jump straight to a topic you need now.


Index Alphabetical list of terms → page numbers. Handy when you remember a keyword but not the chapter.


Glossary / Quick‑Start Summary Short definitions for common ML/DS jargon. Saves time on re‑reading long explanations each time.


Code Snippets & "Cheat Sheets" Reusable blocks of R code (e.g., data cleaning, model fitting). Copy‑paste to speed up experiments.


Examples / Case Studies Step‑by‑step tutorials for specific problems (e.g., logistic regression on credit scoring). Use as templates for your own data.


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4. How to Apply These Tips in Practice



A. Planning the Workflow




Define the Problem Clearly


- What metric matters? Accuracy, AUC, profit?



Gather & Inspect Data


- Load with `readr::read_csv()` or `data.table::fread()`.



Pre‑process Quickly


- Use `tidyverse` verbs (`mutate`, `filter`) or `data.table` for speed.


B. Model Building



Step R Package Typical Code Snippet


Data Splitting `rsample` (or `caret::createDataPartition`) `bootstraps ]
> For the generalised inverse (or quantile) function
> [
> G(u)=F_X^-1(u)\;=\;\infx\in\mathbb R:\, F_X(x)\ge u, \qquad 0< ]
> we have
> [
> X=G(U) \quad\texta.s.
> ]
> where \(U\sim
m Unif(0,1)\).

Thus every real‑valued random variable can be written as a measurable functi