Regularization. Regularization is a regression technique that prevents or regulates the estimated coefficient from shrinking to zero. In other words, one does not encourage the formulation of more complex or flexible models to reduce the ...

Machine Learning-R-squared (R²) It calculates the proportion of the variation in your dependent variable that can be explained by all of the independent variables in the model. It is assumed that each independent variable in ...

The 2 Main Problems in Machine learning Overfitting and underfitting are the two most typical machine learning problems that affect model performance. Before we get into overfitting and underfitting, let’s establish some crucial concepts that ...

linear regression The term “regression” and the methods used to investigate connections between two variables may have originated around 100 years ago. It was first developed in 1908 by Francis Galton, a well-known British biologist ...

DataScience Machine Learning Supervised Learning is a class of Machine learning where the model is trained on a labeled data set. It is referred to as “labeled” because each of the samples used for training ...