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
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 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
In machine learning, normalization is a statistical technique by which the data in a dataset are transformed to have values in a normal (Gaussian) distribution. For each value x in the dataset, its corresponding normalized value x' is calculated in the value range [0,1] as follows. Alternatively, there can be a mean normalization, with normalized ... Read more
In machine learning, regularization is a method by which the ML model cost/error function is changed, to include an extra variable called the regularization hyperparameter. There are two basic types of regularization: L1-norm (lasso regression) and L2-norm (ridge regression). The lasso regularization uses the L1 norm parameter. The lasso regularized cost function is calculated as ... Read more
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
In machine learning, standardization is a feature engineering technique by which the dataset features are re-scaled to achieve zero-mean value (μ=0) and unit standard deviation value (σ=1). Each x value in the dataset gets a corresponding x' standardized value, which is calculated as follows. , where μ is the x variable mean and σ is ... Read more
