additive function

Additive functions are functions which satisfy the condition f(x+y) = f(x) + f(y) for any x and y. Additive functions are linear functions.

An algorithm is a series of instructions, either in the form of pseudo-code or in a computer programming language, which aim at solving a problem or at performing some sort of computation. Algorithms are therefore strongly connected to various fields of mathematics and physics, such as probability and statics, calculus and linear algebra. Algorithms apply ... Read more

coefficient of determination

The coefficient of determination (also depicted as R^2) is a calculation of the proportion of the variance in the dependent variable (noted as y) which is explained by a linear regression model. The value of the coefficient of determination can be in the 0..1 range. The coefficient of determination can be calculated by using the ... Read more

convex function

A convex function is a function which features a single global minimum, whereas a non-convex functions presents many local minima. A good analysis on the mathematical aspects and mathematical definition of convex and non-convex functions can be found at: https://rumn.medium.com/convex-vs-non-convex-functions-why-it-matters-in-optimization-for-machine-learning-39cd9427dfcc. Some examples of ML algorithms which have restrictions related to convex functions are the following: ... Read more

Lambda function

Lambda functions are also known as anonymous functions. Lambda functions are to higher order functions to literals are to computer programming data types. Examples of lambda functions in Python can be found at: https://www.w3schools.com/python/python_lambda.asp.

linear regression

Linear regression is a statistical modeling technique. It utilizes a dependent variable, which can be discrete or continuous number and one independent variable. Linear regression models are simple to train by using machine learning. In many cases are the first type of machine learning models to try to train when using a new data set. ... Read more

logit-transformation

Logit-transformation (aka as the odds ratio) is a logarithmic transformation which is used to transform proportional values into continues values in ML regression problems. The logit-transformation is defined by the following mathematical formula: logit(p)=log(p/(1-p)).

MAE

MAE in statistics and Machine Learning (ML) stands for the Mean Absolute Error. MAE is the average of the sum of the differences between the actual and predicted values in a dataset. In other words the MAE is the calculation of the the average of the residuals. MAE is expressed by the following mathematical formula. ... Read more

MSE

MSE in Machine Learning (ML) stands for Mean Squared Error and is an error calculation formula. MSE calculates the average value of the square power of the sum of differences between the original and predicted values in a dataset. It is similar to MAE, in that MSE is a calculation for the variance of residuals, ... Read more

RMSE

RMSE is simply the root of the MSE statistical metric. RMSE stands for Root Mean Squared Error. The RMSE is in the same metric scale as the observed parameters, same as the MAE metric. RMSE is a calculation for the standard deviation of residuals. Compared to RMSE, MSE is a calculation of the variance of ... Read more

tanh function

The tanh function, also known as the hyperbolic tangent function, is an activation function in artificial neural networks whose output values are constrained between the values of −1 and 1. The following screenshot provides a graph of function f(x)=tanh(x), as output from the Geogebra free online calculator.