cross validation

In machine learning (ML), cross validation is a method in which the data scientists perform an evaluation of an ML model's performance on unlabelled data, i.e. data which the ML model has not seen before. In the method of cross validation, the data which is available in the dataset is split into multiple subsets. One ... Read more


In machine learning, holdout validation is a data sampling method in which the dataset is split into two: the training dataset and the test. The split is equal, i.e. training is performed on the 50% of the dataset and testing is performed on the remaining 50% of the dataset. Holdout validation is not recommended in ... Read more

Machine learning model

A model, under a more general perspective, is the mathematical representation of a real-life system, e.g. a simulator or emulator of an airplane uses airplane flight navigation models and turbo jet engine models. A machine learning (ML) model is a mathematical representation of data applied to an algorithm.


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