Supervised Learning

Supervised learning is a type of machine learning where the model learns from labeled training data to make predictions on new, unseen data.

Overview

Key Concepts

  • Training Data: Input features and corresponding target labels
  • Model Training: Learning the mapping from inputs to outputs
  • Prediction: Using the trained model on new data
  • Error Measurement: Evaluating prediction accuracy

Classification

Binary Classification

  • Decision Boundaries
  • Class Probabilities
  • Threshold Selection
  • Imbalanced Classes

Multiclass Classification

  • One-vs-All
  • One-vs-One
  • Error-Correcting Output Codes
  • Hierarchical Classification

Common Algorithms

  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Support Vector Machines
  • Neural Networks

Regression

Linear Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Regularization Techniques

Non-linear Regression

  • Decision Trees for Regression
  • Random Forest Regression
  • Support Vector Regression
  • Neural Networks for Regression

Model Evaluation

Classification Metrics

  • Accuracy
  • Precision and Recall
  • F1 Score
  • ROC and AUC
  • Confusion Matrix

Regression Metrics

  • Mean Squared Error
  • Root Mean Squared Error
  • Mean Absolute Error
  • R-squared
  • Adjusted R-squared

Advanced Topics

Ensemble Methods

  • Bagging
  • Random Forests
  • Boosting (AdaBoost, XGBoost)
  • Stacking

Neural Networks

  • Feedforward Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Transfer Learning

Handling Challenges

  • Feature Selection
  • Dimensionality Reduction
  • Imbalanced Datasets
  • Missing Data
  • Outlier Detection

Best Practices

Model Selection

  • Cross-validation
  • Hyperparameter Tuning
  • Model Comparison
  • Learning Curves

Deployment

  • Model Serialization
  • Online Learning
  • Model Monitoring
  • Performance Optimization