Supervised Learning Algorithms

Supervised learning algorithms are a type of machine learning technique where a model is trained on labelled data.

In this approach, each training example is associated with a corresponding output label, and the goal is for the model to learn the mapping from inputs to outputs. After training, the model can be used to predict labels for unseen data.

Here are some common supervised learning algorithms:

1. Linear Regression
   
   • Type: Regression
   • Usage: Predicting continuous values (e.g., house prices, stock prices).
   • How it works: It finds the best-fit line that minimizes the difference between predicted and actual values.

2. Logistic Regression
   
   • Type: Classification
   • Usage: Binary classification tasks (e.g., spam detection, disease diagnosis).
   • How it works: It models the probability that an instance belongs to a particular class using the logistic function.

3. Support Vector Machines (SVM)
   
   • Type: Classification, but can also be used for regression (SVR)
   • Usage: Text classification, image recognition.
   • How it works: It finds a hyperplane that maximally separates the classes in the feature space. For non-linearly separable data, it uses kernels to map input features into higher-dimensional spaces.

4. k-Nearest Neighbours (k-NN)
   
   • Type: Classification and Regression
   • Usage: Handwriting detection, image recognition.
   • How it works: It assigns the label of the majority class among the k nearest neighbours of a test instance.

5. Decision Trees
   
   • Type: Classification and Regression
   • Usage: Credit scoring, medical diagnosis.
   • How it works: A tree-like model is created where nodes represent features and branches represent decision rules that split the data until a prediction is made.

6. Random Forest
   
   • Type: Classification and Regression
   • Usage: Fraud detection, recommendation systems.
   • How it works: It combines multiple decision trees (an ensemble) to make more robust predictions by averaging or voting across all trees.

7. Gradient Boosting Machines (GBM)
   
   • Type: Classification and Regression
   • Usage: Winning many machine learning competitions, stock price prediction.
   • How it works: It builds an ensemble of weak learners (typically decision trees) sequentially, where each new tree tries to correct the errors of the previous one.

8. Naive Bayes
   
   • Type: Classification
   • Usage: Text classification, spam detection.
   • How it works: Based on Bayes’ Theorem, this algorithm assumes the features are conditionally independent given the class label and uses probabilities for classification.

9. Artificial Neural Networks (ANNs)
   
   • Type: Classification and Regression
   • Usage: Image recognition, language translation.
   • How it works: Inspired by biological neurons, ANNs consist of interconnected nodes (neurons) that pass signals and learn from data through weights and activation functions.

10. XGBoost (Extreme Gradient Boosting)
    
    • Type: Classification and Regression
    • Usage: High performance in Kaggle competitions, risk prediction.
    • How it works: A fast and efficient version of gradient boosting that reduces bias and variance more effectively.

11. Lasso and Ridge Regression
    
    • Type: Regression
    • Usage: Feature selection, predicting continuous values with penalized regression.
    • How it works: Regularized versions of linear regression, with Lasso performing feature selection by penalizing the absolute values of coefficients (L1 penalty), while Ridge shrinks coefficients by penalizing their squares (L2 penalty).

Each algorithm has strengths and weaknesses depending on the dataset and problem. For example, random forests and gradient boosting are robust to overfitting but can be slower, while simpler algorithms like logistic regression may be faster but less powerful for complex data.