Bootstrapping Validation (RapidMiner Studio Core)

Synopsis

This operator performs validation after bootstrapping a sampling of training data set in order to estimate the statistical performance of a learning operator (usually on unseen data sets). It is mainly used to estimate how accurately a model (learnt by a particular learning operator) will perform in practice.

Description

The Bootstrapping Validation operator is a nested operator. It has two subprocesses: a training subprocess and a testing subprocess. The training subprocess is used for training a model. The trained model is then applied in the testing subprocess. The performance of the model is also measured during the testing phase. The training subprocess must provide a model and the testing subprocess must provide a performance vector.

The input ExampleSet is partitioned into two subsets. One subset is used as the training set and the other one is used as the test set. The size of two subsets can be adjusted through the sample ratio parameter. The sample ratio parameter specifies the ratio of examples to be used in the training set. The ratio of examples in the testing set is automatically calculated as 1-n where n is the ratio of examples in the training set. The important thing to note here is that this operator performs bootstrapping sampling (explained in the next paragraph) on the training set before training a model. The model is learned on the training set and is then applied on the test set. This process is repeated m number of times where m is the value of the number of validations parameter.

Bootstrapping sampling is sampling with replacement. In sampling with replacement, at every step all examples have equal probability of being selected. Once an example has been selected for the sample, it remains candidate for selection and it can be selected again in any other coming steps. Thus a sample with replacement can have the same example multiple number of times. More importantly, a sample with replacement can be used to generate a sample that is greater in size than the original ExampleSet.

Usually the learning process optimizes the model parameters to make the model fit the training data as well as possible. If we then take an independent sample of testing data, it will generally turn out that the model does not fit the testing data as well as it fits the training data. This is called 'over-fitting', and is particularly likely to happen when the size of the training data set is small, or when the number of parameters in the model is large. Bootstrapping Validation is a way to predict the fit of a model to a hypothetical testing set when an explicit testing set is not available.

Differentiation

Split Validation

Its validation subprocess executes just once. It provides linear, shuffled and stratified sampling.

Cross Validation

The input ExampleSet is partitioned into k subsets of equal size. Of the k subsets, a single subset is retained as the testing data set (i.e. input of the testing subprocess), and the remaining k − 1 subsets are used as the training data set (i.e. input of the training subprocess). The cross-validation process is then repeated k times, with each of the k subsets used exactly once as the testing data. The k results from the k iterations can then be averaged (or otherwise combined) to produce a single estimation.

Input

training (IOObject)
This input port expects an ExampleSet for training a model (training data set). The same ExampleSet will be used during the testing subprocess for testing the model.

Output

model (Model)
The training subprocess must return a model, which is trained on the input ExampleSet. Please note that model built on the complete input ExampleSet is delivered from this port.
training (IOObject)
The ExampleSet that was given as input at the training input port is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
averagable (Performance Vector)
The testing subprocess must return a Performance Vector. This is usually generated by applying the model and measuring its performance. Two such ports are provided but more can also be used if required. Please note that the statistical performance calculated by this estimation scheme is only an estimate (instead of an exact calculation) of the performance which would be achieved with the model built on the complete delivered data set.

Parameters

number_of_validationsThis parameter specifies the number of times the validation should be repeated i.e. the number of times the inner subprocess should be executed. Range: integer
sample_ratioThis parameter specifies the relative size of the training set. In other validation schemes this parameter should be between 1 and 0, where 1 means that the entire ExampleSet will be used as training set. In this operator its value can be greater than 1 because bootstrapping sampling can generate an ExampleSet with a number of examples greater than the original ExampleSet. All examples that are not selected for the training set are automatically selected for the test set. Range: real
use_weightsIf this parameter is checked, example weights will be used for bootstrapping if such weights are available. Range: boolean
average_performances_onlyThis parameter indicates if only performance vectors should be averaged or all types of averagable result vectors. Range: boolean
use_local_random_seedThis parameter indicates if a local random seed should be used for randomizing examples of a subset. Using the same value of the local random seed will produce the same samples. Changing the value of this parameter changes the way examples are randomized, thus samples will have a different set of examples. Range: boolean
local_random_seedThis parameter specifies the local random seed. This parameter is only available if the use local random seed parameter is set to true. Range: integer

Tutorial Processes

Validating Models using Bootstrapping Validation

The 'Golf' data set is loaded using the Retrieve operator. The Generate ID operator is applied on it to uniquely identify examples. This is done so that you can understand this process easily; otherwise IDs are not required here. A breakpoint is added after this operator so that you can preview the data before the application of the Bootstrapping Validation operator. You can see that the ExampleSet has 14 examples with ids from 1 to 14. Double click the Bootstrapping Validation operator and you will see the training and testing subprocesses. The Decision Tree operator is used in the training subprocess. The trained model (i.e. Decision Tree) is passed to the testing subprocess through the model ports. The testing subprocess receives testing data from the testing port.

Now, have a look at the parameters of the Bootstrapping Validation operator. The no of validations parameter is set to 2 thus the inner subprocess will execute just twice. The sample ratio parameter is set to 0.5. The number of examples in the ExampleSet is 14 and sample ratio is 0.5, thus the training set will be composed of 7 (i.e. 14 x 0.5) examples. But it is not necessary that these examples will be unique because bootstrapping sampling can select an example multiple number of time. All the examples that are not selected for the training set automatically become part of the testing set. You can verify this by running the process. You will see that the training set has 7 examples but they are not all unique and all the examples that were not part of the training set are part of the testing set.

Categories

Versions