Supervised Learning

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Regression

Regression is a supervised learning technique used for predicting continuous numerical outcomes based on input data. This category includes various types of regression models such as linear regression, polynomial regression, and ridge regression. These models establish a relationship between the dependent variable and one or more independent variables, enabling predictions of values within a continuous range. Regression is widely used in fields like economics, finance, and environmental science for tasks such as predicting stock prices, housing prices, and temperature trends.

Classification

Classification is a supervised learning technique used to categorize data into predefined classes or labels. This category covers various classification algorithms such as logistic regression, decision trees, support vector machines, and k-nearest neighbors. Classification models are trained on labeled data and used to predict the class of new, unseen instances. Applications include spam detection, image recognition, medical diagnosis, and sentiment analysis, where the goal is to assign inputs to discrete categories.

Decision Trees

Decision trees are a type of supervised learning algorithm used for both regression and classification tasks. This category involves building tree-like models of decisions based on the features of the input data. Decision trees split the data into branches based on feature values, leading to a final decision or prediction. They are popular due to their simplicity, interpretability, and effectiveness in handling both numerical and categorical data. Decision trees are used in various applications, including credit scoring, customer segmentation, and fault diagnosis.

Ensemble Methods

Ensemble methods involve combining multiple learning algorithms to improve the performance of a model. This category includes techniques such as bagging, boosting, and stacking. Popular ensemble algorithms include Random Forest, which combines multiple decision trees, and Gradient Boosting Machines (GBM), which build models sequentially to correct errors from previous models. Ensemble methods enhance prediction accuracy and robustness by leveraging the strengths of multiple models, making them effective in a wide range of applications.

Neural Networks

Neural networks are a class of supervised learning models inspired by the human brain, consisting of layers of interconnected nodes (neurons). This category focuses on neural networks designed for supervised tasks, including feedforward neural networks, convolutional neural networks (CNNs) for image classification, and recurrent neural networks (RNNs) for sequence prediction. Neural networks are powerful tools for handling complex patterns in data and are widely used in applications like image recognition, natural language processing, and time series forecasting.