Entropy

Supervised Learning Algorithms

Supervised learning is one of the most commonly used approaches in Machine Learning, where models learn from labeled datasets. This means that the input data has corresponding correct outputs, allowing the model to learn relationships and patterns over time. Supervised learning is widely used for tasks such as classification and regression.

Classification Algorithms

Classification involves categorizing data into predefined classes. Some of the most popular classification algorithms include:

  • Logistic Regression – Despite its name, this is a classification algorithm used for binary classification tasks. It applies a sigmoid function to predict probabilities and is widely used in medical diagnoses and spam detection.
  • Decision Trees – A tree-like model where data is split based on feature values. Decision trees are easy to interpret but prone to overfitting. They are often used in finance and customer segmentation.
  • Random Forest – An ensemble learning technique that combines multiple decision trees to improve accuracy and reduce overfitting. It is effective in handling both classification and regression tasks.
  • Support Vector Machines (SVM) – A powerful algorithm that finds the best decision boundary to separate data points into different classes. It is effective for high-dimensional data and is commonly used in image recognition.
  • Neural Networks – Inspired by the human brain, neural networks consist of layers of interconnected nodes (neurons). Deep learning architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for tasks like image recognition and natural language processing.

Regression Algorithms

Regression models predict continuous numerical values. Some widely used regression algorithms include:

  • Linear Regression – The simplest regression model that finds a linear relationship between independent and dependent variables. It is used in predicting sales, housing prices, and stock market trends.
  • Ridge and Lasso Regression – Variants of linear regression that add regularization to prevent overfitting. Ridge regression uses L2 regularization, while Lasso regression uses L1 regularization.
  • Polynomial Regression – An extension of linear regression that fits a polynomial equation to the data. It is useful when relationships between variables are non-linear.
  • Gradient Boosting Machines (GBM) – An ensemble technique that improves predictive performance by combining multiple weak models, such as XGBoost, LightGBM, and CatBoost.

Supervised learning algorithms are powerful tools for real-world applications like fraud detection, medical diagnosis, and predictive analytics. Selecting the right algorithm depends on factors such as data size, complexity, and interpretability requirements.