Artificial Intelligence and Machine Learning
AI and ML
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 (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)).
In machine learning, LOOCV stands for leave-one-out cross validation. Assuming that the dataset of an ML project comprises n examples (rows), we split the dataset into n subsets and perform n iterations of training/testing. In each iteration, n-1 examples are used as training data and only 1 example is used the testing subset. This process ... Read more
LSTM stands for Long Short-Term Memory. LSTM is a type of RNN artificial neural network, which uses a complex formula to implement an architecture of memory cells, which makes decisions about what pieces of historical information (states) to preserve and which to drop.