📘 Supervised Machine Learning: Complete Guide Supervised Machine Learning (ML) is a type of machine learning where the model is trained on a labeled dataset, meaning each training example is paired with an output label. The goal is to learn a mapping from inputs to outputs that can be used to predict outcomes for unseen data.
📖 What is Supervised Learning? In supervised learning, the model is provided with input-output pairs so it can learn to predict the output from the input. It uses feedback (label) to adjust and improve predictions.
Goal: Learn a function f(x) → y from data.
🔍 Types of Supervised Learning
- Linear Regression
- Ridge and Lasso
- Elastic Net
- Logistic Regression(classification)
- Decision Tree
- Random Forest
- Ada Boost
- Xg Boost
🔧 Popular Algorithms Task Algorithm Examples Classification Logistic Regression, Decision Trees, Random Forest, SVM, k-NN, Naive Bayes, Neural Networks Regression Linear Regression, Ridge/Lasso, SVR, Random Forest Regressor, Gradient Boosting, XGBoost, Neural Networks
Workflow of Supervised ML Collect Data Preprocess Data (handle missing values, encoding, scaling) Split Dataset (Train/Test/Validation) Choose Algorithm Train Model Evaluate Model Fine-Tune (Hyperparameter Optimization) Deploy
Evaluation Metrics For Classification: Accuracy Precision, Recall, F1-Score ROC-AUC Confusion Matrix For Regression: Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE) R² Score