Last Updated on June 30, 2020 It is critical that any data preparation performed on a training dataset is also performed on a new dataset in the future. This may include a test dataset when evaluating a model or new data from the domain when using a model to make predictions. Typically, the model fit on the training dataset is saved for later use. The correct solution to preparing new data for the model in the future is to also […]

Last Updated on August 18, 2020 Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable. Feature selection is often straightforward when working with real-valued data, such as using the Pearson’s correlation coefficient, but can be challenging when working with categorical data. The two most commonly used feature selection methods for categorical input data when the target variable is also categorical (e.g. classification predictive modeling) are the chi-squared […]

Last Updated on August 20, 2020 Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model. Statistical-based feature selection methods involve evaluating the relationship between each input variable and the target variable using statistics and selecting those input variables that have the strongest relationship […]