Supervised Learning
Master classification, regression, and practical applications
Understanding Supervised Learning
Supervised learning is a machine learning approach where the model learns from labeled data. The algorithm is trained on a dataset where the correct output is known, allowing it to learn patterns and make predictions on new, unseen data.
Types of Supervised Learning
Classification
Predicting discrete categories or classes
Regression
Predicting continuous numerical values
Classification Algorithms
- • Logistic Regression
- • Support Vector Machines
- • Decision Trees
- • Random Forests
- • K-Nearest Neighbors
Regression Algorithms
- • Linear Regression
- • Polynomial Regression
- • Ridge Regression
- • Lasso Regression
- • Elastic Net
Performance Metrics
Classification Metrics
- • Accuracy
- • Precision & Recall
- • F1 Score
- • ROC & AUC
Regression Metrics
- • Mean Squared Error (MSE)
- • Root Mean Squared Error (RMSE)
- • Mean Absolute Error (MAE)
- • R-squared (R²)
Implementation Example
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# Prepare data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create and train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
# Evaluate performance
accuracy = accuracy_score(y_test, predictions)
print(f"Model Accuracy: {accuracy:.2f}")