Given a dataset, encodes categorical fields using ordinal encoding, which uses a single column of integers to represent field classes (levels). It then creates a new dataset, with additional fields containing ordinal encodings of the categorical fields.

If classes have a known order (such as Like, Somewhat Like, Neutral, Somewhat Dislike, and Dislike), the integer mapping can be supplied; otherwise, integers are assigned by class count, in descending order (in the case of ties, classes are ordered alphabetically).

For more information, please see the readme.

Extends a dataset with the distance in meters between lat/long fields and a reference point.

5-fold cross-validation of a model created with default parameters

Script for ensemble optimization using SMACdown. The script can be used for both classification and regression ensembles, provided you select the appropriate metric to be optimized (see below).

Classification metrics:

• average_recall

• average_phi

• accuracy

• average_precision

• average_f_measure

Regression metrics:

• r_squared.

Find the closest cluster rows to a given one

The objective of this script is to perform a k-fold cross validation of a logistic regression built from a dataset. The algorithm:

• Divides the dataset in k parts
• Holds out the data in one of the parts and builds a logistic regression with the rest of data
• Evaluates the logistic regression with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.

The objective of this script is to perform a k-fold cross validation of an ensemble built from a dataset. The algorithm:

• Divides the dataset in k parts
• Holds out the data in one of the parts and builds an ensemble with the rest of data
• Evaluates the ensemble with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.

5-fold cross-validation of a model created with default parameters

The objective of this script is to perform a k-fold cross validation of an already built Supervised Learning Model. The algorithm:

• Retrives the dataset and configuration of the model
• Divides the dataset in k parts
• Holds out the data in one of the parts and builds a supervised model with the rest of data
• Evaluates the model with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.