Glossary

**Random Forest**: An ensemble learning method that combines multiple decision trees to improve classification or regression accuracy by averaging their predictions and reducing overfitting.

**Recurrent Neural Network (RNN)**: A type of neural network designed for sequence data, where connections between nodes form a directed graph along a temporal sequence, allowing information to persist across time steps.

**Regularization**: A technique used in machine learning to prevent overfitting by adding a penalty to the loss function, such as L1 (lasso) or L2 (ridge) regularization, to encourage simpler models.

**Reinforcement Learning (RL)**: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards, often using policies and value functions.

**Residual Neural Network (ResNet)**: A deep learning architecture that introduces shortcut connections or residuals to allow for easier training of very deep networks, helping to prevent the vanishing gradient problem.

**Ridge Regression**: A type of linear regression that includes a regularization term (L2 penalty) to shrink the regression coefficients, reducing multicollinearity and improving model stability.

**Robustness**: The ability of a machine learning model to maintain performance when faced with noisy, missing, or adversarial data, ensuring reliability across different scenarios.

**ROC Curve (Receiver Operating Characteristic Curve)**: A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied, showing the trade-off between true positive rate and false positive rate.

**Root Mean Squared Error (RMSE)**: A measure of the differences between predicted and observed values, calculated as the square root of the average of the squared differences, often used to evaluate regression models.

**Random Search**: A hyperparameter optimization technique where hyperparameters are selected randomly from a predefined distribution, offering a simpler alternative to grid search and often finding better solutions faster.

**Regression Analysis**: A statistical method used to model the relationship between a dependent variable and one or more independent variables, commonly used for prediction and forecasting.

**ReLU (Rectified Linear Unit)**: A popular activation function used in neural networks that outputs the input directly if it is positive, otherwise, it outputs zero, helping to solve the vanishing gradient problem.

**Recurrent Unit**: The basic component of a recurrent neural network, responsible for processing and storing information from the sequence, often using structures like LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit).

**Reinforcement Learning Policy**: A strategy used by an agent in reinforcement learning to decide the next action based on the current state, which can be deterministic or stochastic.

**Residual Block**: A building block of ResNet architectures that includes a skip connection to allow gradients to bypass certain layers, making it easier to train deep neural networks.

**Risk Minimization**: The process of selecting a model that minimizes expected loss, often involving trade-offs between bias, variance, and model complexity.

**Ridge Polynomial Network**: A neural network variant where the activation function is replaced by a polynomial, allowing the model to learn higher-order interactions between inputs.

**RNN Encoder-Decoder**: A neural network architecture often used in sequence-to-sequence tasks, where an encoder processes the input sequence into a fixed-length vector and a decoder generates the output sequence from this vector.

**Random Walk**: A mathematical process that describes a path consisting of a series of random steps, often used in modeling time series data, financial markets, and graph traversal.

**Robust Statistics**: Statistical methods that are not unduly affected by outliers or small departures from model assumptions, often used to create more reliable models.

**Rank Aggregation**: The process of combining multiple rankings into a single aggregated ranking, often used in information retrieval, recommendation systems, and social choice theory.

**Regularization Path**: A sequence of models trained with varying amounts of regularization, allowing for the selection of the optimal regularization parameter by evaluating model performance across the path.

**Representation Learning**: A type of machine learning where the model automatically discovers the features or representations from raw data, often used in deep learning to extract meaningful features from images, text, or audio.

**Rejection Sampling**: A technique used in Monte Carlo methods to generate samples from a target distribution by sampling from a proposal distribution and rejecting some samples based on a criterion.

**Residual Sum of Squares (RSS)**: A measure of the discrepancy between the data and an estimation model, calculated as the sum of the squares of the residuals (differences between observed and predicted values).

**Randomized SVD (Singular Value Decomposition)**: A faster approximation of the singular value decomposition, particularly useful for large-scale data, where the full SVD computation would be too expensive.

**RANSAC (Random Sample Consensus)**: An iterative method used to estimate parameters of a mathematical model from a dataset that contains outliers, often used in computer vision for tasks like image alignment and object recognition.

**Radial Basis Function (RBF)**: A real-valued function whose value depends only on the distance from a central point, commonly used in support vector machines and kernel methods to model complex, non-linear relationships.

**Resource Allocation**: The process of distributing resources efficiently among competing tasks or processes, often modeled using optimization techniques in operations research and computer science.

**Reinforcement Learning Environment**: The setting in which an RL agent operates, including the states, actions, rewards, and transitions that define how the agent interacts with the world.

**Resampling**: A statistical method that involves drawing repeated samples from the observed data, often used in methods like bootstrapping and cross-validation to assess the variability of a model.

**Restricted Boltzmann Machine (RBM)**: A type of stochastic neural network that can learn a probability distribution over its set of inputs, often used for dimensionality reduction, feature learning, and collaborative filtering.

**Regression Tree**: A decision tree used for regression tasks, where the target variable is continuous, and the tree is built by recursively splitting the data into subsets that minimize the variance of the target variable within each subset.

**Reinforcement Learning Reward**: The signal received by an agent in reinforcement learning that indicates the success or failure of its actions, guiding the learning process to achieve better outcomes.

**Redundancy Reduction**: A technique used in feature selection and data preprocessing to remove redundant features or information, improving the efficiency and performance of machine learning models.

**Reversible Neural Network**: A type of neural network where the transformations are invertible, allowing the model to recover the input from the output, often used to reduce memory usage during training.

**Ridge Polynomial Network**: A neural network variant where the activation function is replaced by a polynomial, allowing the model to learn higher-order interactions between inputs.

**Robust Optimization**: An approach to optimization that seeks to find solutions that are not only optimal but also robust to uncertainty or variations in the problem parameters.

**RNN with Attention**: A recurrent neural network architecture enhanced with an attention mechanism, allowing the model to focus on specific parts of the input sequence when making predictions.

**Receiver Operating Characteristic (ROC) Curve**: A graphical plot used to evaluate the diagnostic ability of a binary classifier system, showing the trade-off between the true positive rate and the false positive rate as the decision threshold varies.

**Recursive Feature Elimination (RFE)**: A feature selection method that recursively removes the least important features based on the model’s performance, often used to improve model accuracy and reduce complexity.

**Regularized Logistic Regression**: A variant of logistic regression that includes a regularization term (L1 or L2 penalty) to prevent overfitting and improve generalization on new data.

**Risk Assessment**: The process of identifying, analyzing, and evaluating potential risks in a system or decision, often using statistical and machine learning models to predict and mitigate adverse outcomes.