Script to select the n best features for modeling a given dataset,
using a greedy algorithm:
Initialize the set S of selected features to the empty set
Split your dataset into training and test sets
For i in 1 ... n:
For each feature f not in S, model and evaluate with feature set S + f
Greedily select the feature f' with the best performance and add it to S
The script takes as inputs the dataset to use and the number of features (that is, dataset fields) to return and yields as output a list of the n selected features, as field identifiers.
To select the best performance, the script uses the metric
average_phi in the evaluations it performs, which is only available for classification problems. Therefore, the script is only valid for categorical objective fields.
Script to select the n best features for modeling a given dataset,
using a greedy algorithm:
Initialize the set S of selected features to the empty set
Split your dataset into training and test sets
For i in 1 ... n:
For each feature f not in S, model and evaluate with feature set S + f
Greedily select the feature f' with the best performance and add it to S
The script takes as inputs the dataset to use and the number of features (that is, dataset fields) to return and yields as output a list of the n selected features, as field identifiers.
To select the best performance, the script uses the metric
average_phi in the evaluations it performs, which is only available for classification problems. Therefore, the script is only valid for categorical objective fields.
Script to select the n best features for modeling a given dataset,
using a greedy algorithm:
Initialize the set S of selected features to the empty set
Split your dataset into training and test sets
For i in 1 ... n:
For each feature f not in S, model and evaluate with feature set S + f
Greedily select the feature f' with the best performance and add it to S
The script takes as inputs the dataset to use and the number of features (that is, dataset fields) to return and yields as output a list of the n selected features, as field identifiers.
To select the best performance, the script uses the metric
average_phi in the evaluations it performs, which is only available for classification problems. Therefore, the script is only valid for categorical objective fields.
