Supervised Learning Cheatsheets

Welcome to the Supervised Learning section of the Algorithm Cheatsheets repository!

Supervised Learning is a subcategory of Machine Learning where algorithms are trained using labeled datasets. The model learns to map an input to an output based on historical examples. Once trained, the model can predict the output for new, unseen data.

Core Concepts

Supervised Learning algorithms are generally split into two main tasks based on the type of output variable they predict:

1. Classification: Predicting a discrete class label (e.g., "Spam" vs. "Not Spam", or "Cat" vs. "Dog").
2. Regression: Predicting a continuous numerical value (e.g., House Prices, Temperature, or Stock Prices).

Directory Contents

Here is what you will find in this directory. Each algorithm has a theoretical Markdown cheatsheet and an accompanying Jupyter Notebook with Python code (scikit-learn, xgboost, etc.).

Regression Algorithms

• Linear Regression: Cheatsheet
  • Best for: Simple, linear relationships and establishing a baseline.
• Polynomial Regression: Cheatsheet
  • Best for: Non-linear, curved relationships using linear math.

Classification (and Regression) Algorithms

• Logistic Regression: Cheatsheet
  • Best for: Binary classification and outputting probabilities.
• K-Nearest Neighbors (KNN): Cheatsheet
  • Best for: Simple clustering tasks where distance equals similarity.
• Support Vector Machines (SVM): Cheatsheet
  • Best for: Complex, high-dimensional data with clear margins of separation.
• Naive Bayes: Cheatsheet
  • Best for: Text classification, spam filtering, and NLP baselines.
• Decision Trees: Cheatsheet
  • Best for: Highly interpretable, rule-based decision making.

Ensemble Methods

• Random Forest: Cheatsheet
  • Best for: High accuracy out-of-the-box, robust to overfitting, handling tabular data.
• Boosting (AdaBoost, XGBoost): Cheatsheet
  • Best for: Winning Kaggle competitions, squeezing maximum performance from structured data.

How to use this section

• For Theory: Read the .md files. They contain the mathematical intuition, pros and cons, and real-world use cases.
• For Practice: Open the code/ folder to see how to import, train, predict, and evaluate these models using standard Python libraries.

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