# Rotational invariance

What is Rotational invariance : Rotational invariance is a property of a physical system or mathematical equation that remains unchanged under rotations. This means that if a system or equation exhibits rotational invariance, it will not be affected by rotations in any way. There are many examples of rotational invariance in physics, but we will […]

# Root Mean Squared Error (RMSE)

What is Root Mean Squared Error (RMSE) : Root Mean Squared Error (RMSE) is a measure of the difference between the predicted value and the actual value of a quantity. It is used in a variety of fields, including statistics, machine learning, and engineering, to evaluate the performance of predictive models. The RMSE is calculated […]

# Residual

What is Residual : Residual refers to the remaining or leftover value or amount after certain calculations or processes have been completed. It can be used in a variety of contexts, including finance, statistics, and engineering. One example of residual is in the field of finance, specifically in the calculation of capital expenditures. Capital expenditures […]

# Resampling

What is Resampling : Resampling is a statistical method used to analyze and understand a dataset by generating new samples from it. It allows for the estimation of statistical properties, such as mean and variance, and can be used to test hypotheses, assess model performance, and select the best models for a given dataset. There […]

# Relational Database

What is a Relational Database : A relational database is a type of database that stores data in a structured format, using tables and relationships between them. It is called “relational” because the tables are related to each other through common columns, known as keys. An example of a relational database is a customer database […]

# Reinforcement Learning (RL)

What is Reinforcement Learning (RL) : Reinforcement learning (RL) is a type of machine learning that involves teaching an agent to make decisions in an environment in order to maximize a reward. The agent learns through trial and error, receiving positive or negative reinforcement based on its actions and their consequences. RL algorithms can be […]

# Regularization

What is Regularization : Regularization is a technique used in machine learning to prevent overfitting, which is when a model fits too closely to the training data and performs poorly on unseen data. There are two main types of regularization: L1 and L2. L1 regularization, also known as Lasso, is a method that adds a […]

# Regression analysis

What is Regression analysis : Regression analysis is a statistical technique used to examine the relationship between two or more variables. It is often used to predict the value of one variable (the dependent variable) based on the value of another variable (the independent variable). For example, consider a company that wants to understand the […]

# Regression

Regression : Regression is a statistical technique that is used to predict a continuous outcome based on one or more predictor variables. It is a widely used tool in many fields, including economics, finance, and marketing, to name a few. To understand regression, it is helpful to start with an example. Suppose you are a […]

# Recall

Recall : Recall is a metric used to evaluate the performance of a machine learning model, specifically in classification tasks. It is defined as the number of true positive predictions made by the model, divided by the total number of positive instances in the test set. In other words, it measures the proportion of actual […]