How is the smote function used in DMWR?

How is the smote function used in DMWR?

The function can also be used to obtain directely the classification model from the resulting balanced data set. This can be done by including the name of the R function that implements the classifier in the parameter learner. You may also include other parameters that will be forward to this learning function.

What can you do with the smote function?

Namely, it can generate a new “SMOTEd” data set that addresses the class unbalance problem. Alternatively, it can also run a classification algorithm on this new data set and return the resulting model.

How is smote used for unbalanced classification problems?

View source: R/smote.R. This function handles unbalanced classification problems using the SMOTE method. Namely, it can generate a new “SMOTEd” data set that addresses the class unbalance problem. Alternatively, it can also run a classification algorithm on this new data set and return the resulting model.

How to do rdocumentation with DMWR package?

Obtains a summary statistic of one of the evaluation metrics used in an experimental comparison, for all learners and data sets involved in the comparison. Obtain the predictions of a model using a sliding window learning approach. Provide a ranking of learners involved in an experimental comparison.

How to create a smote function in R?

This can be done by including the name of the R function that implements the classifier in the parameter learner. You may also include other parameters that will be forward to this learning function. If the learner parameter is not NULL (the default) the returning value of the function will be the learned model and not the balanced data set.

When to use parameter k in smote function?

The parameter k controls how many of these neighbours are used. The function can also be used to obtain directely the classification model from the resulting balanced data set. This can be done by including the name of the R function that implements the classifier in the parameter learner.

How is the smote function used in classification?

This function handles unbalanced classification problems using the SMOTE method. Namely, it can generate a new “SMOTEd” data set that addresses the class unbalance problem. Alternatively, it can also run a classification algorithm on this new data set and return the resulting model.