Machine Learning Cheat Sheet, Supervised ML Algorithms, Decision Trees, k-NN and SVM, Visual Study Guide

Which machine learning algorithm should you try first? This visual guide shows eight algorithms on the same data, and a simple loop for choosing between them.
THIS IS A DIGITAL DOWNLOAD. NO PHYSICAL ITEM WILL BE SHIPPED.
You will receive printable PDF files in one ZIP file. No printed or laminated sheets are included. The photos show how they look once printed.

SEE WHAT EACH ALGORITHM DOES WITH YOUR DATA
A study guide to supervised machine learning on two sheets, printed on both sides. It is classification-focused, with an introduction to regression. Every algorithm is shown on the same small data set: will a plant thrive, given how much water and sun it gets? Every boundary picture and every accuracy is computed from the real fitted models, so you can compare them side by side. No code on the sheet: pictures, a little maths and decision charts.

SHEET 1, FRONT - WHICH ALGORITHM SHOULD I TRY?
- supervised learning in one picture: features, labels, regression or classification
- a starting loop, not a rulebook: baseline first, a simple model, validate, diagnose underfitting or overfitting, adjust, and use the test set once, at the end
- the three data roles: training, validation and test
- why a model must beat the baseline (here a straight line does not)
- common traps

SHEET 1, BACK - SIMPLE MODELS
- linear regression and logistic regression
- naive Bayes and k-nearest neighbours (k = 1 versus k = 3)
- for each: the idea in one sentence, the decision boundary on the plants, the maths in one box, strengths, what it assumes, when it fails, its main setting and its validation accuracy
- distances need comparable units: why k-NN changes when water is measured in millilitres

SHEET 2, FRONT - TREES AND MARGINS
- one decision tree, drawn twice: as yes / no questions and as rectangles on the data
- random forest and gradient boosting
- support vector machine: the margin idea and the RBF kernel boundary
- why an unlimited tree memorises noise

SHEET 2, BACK - COMPARE AND CHOOSE
- eight algorithms side by side: task, boundary shape, scaling needed, interpretability, main setting, typical weak spot
- too simple or too complex: training versus validation accuracy as a tree grows deeper
- "diagnose this": a short scenario with its answer
- where should I start? Sensible first experiments for common situations
- predicting outside the data: what a line and a forest do beyond the last training value

TEST YOURSELF VERSION
The same pages with the answers left blank: name the model from its decision boundary, fill in the loop, the table cells and the diagnosis. Fill them in from memory, then check yourself against the cheat sheet.

WHAT YOU GET: 8 PDF FILES (in one ZIP)
- Cheat Sheet and Test Yourself
- 2 colour styles: Color, and Gray (uses less coloured ink)
- 2 paper sizes: A4 and US Letter
- each file is two sheets: 4 pages, print double-sided ("flip on long edge")
- a short READ ME with printing tips

WHO IT IS FOR
Students in a first machine learning or data science course, junior data scientists and analysts, career changers and self-taught learners who want to understand what the common algorithms do before choosing one. No programming needed to read it; school algebra and a basic idea of probability help.

HOW IT IS MADE
Typeset in LaTeX; every picture and number is computed by code from real fitted models, no AI image generator was used. The plant data is made up for teaching, and the sheet says so. The content and the code were prepared with help from an AI assistant; every number on the sheet was recomputed by a script, and the content went through several independent reviews.

HOW IT WORKS
1. Purchase the listing.
2. Download the ZIP file instantly from Purchases & Reviews in your Etsy account (or from the link in your confirmation email) and unzip it.
3. Print at home or at a print shop at 100% / "actual size", double-sided, or open the PDF on a tablet.

PLEASE NOTE
- This is a digital product. Nothing will be shipped.
- This is a focused, visual study guide to choosing supervised learning algorithms, not a complete machine learning course. Neural networks, clustering and detailed evaluation metrics are not covered.
- Due to the nature of digital downloads, refunds are not accepted. Send us a message if you have any problem with your files and we'll gladly help.
- Colours may vary slightly between screens and printers.
- For personal use only. You may print as many copies as you need for yourself. Teachers: please message us about classroom use. Reselling or sharing the files is not allowed. (c) MIRAICO Academy, all rights reserved.
- Independent educational resource. Not affiliated with or endorsed by the projects mentioned.

Also in the shop: the NumPy and pandas cheat sheets (the tools of data science) and Python Essentials.

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