What is supervised feature selection?
There are supervised feature selection algorithms which identify the relevant features for best achieving the goal of the supervised model (e.g. a classification or a regression problem)and they rely on the availability of labelled data.
What is the difference between PCA and feature selection?
The difference is that PCA will try to reduce dimensionality by exploring how one feature of the data is expressed in terms of the other features(linear dependecy). Feature selection instead, takes the target into consideration.
How are feature selection methods used in datacamp?
Filter methods are generally used as a data preprocessing step. The selection of features is independent of any machine learning algorithm. Features give rank on the basis of statistical scores which tend to determine the features’ correlation with the outcome variable. Correlation is a heavily contextual term, and it varies from work to work.
What’s the difference between dimensionality reduction and feature selection?
Both methods tend to reduce the number of attributes in the dataset, but a dimensionality reduction method does so by creating new combinations of attributes (sometimes known as feature transformation), whereas feature selection methods include and exclude attributes present in the data without changing them.
When to use feature selection in cross validation?
Simply speaking, you should include the feature selection step before feeding the data to the model for training especially when you are using accuracy estimation methods such as cross-validation. This ensures that feature selection is performed on the data fold right before the model is trained.
Which is an example of forward feature selection?
To evaluate the features, the predictive accuracy used for classification tasks and goodness of cluster is evaluated using clustering. Some typical examples of wrapper methods are forward feature selection, backward feature elimination, recursive feature elimination, etc.