4.5 Understanding Reverse Transformations

Understand why you need reverse transformations.

Reverse transformations ensure that information returned by the model is expressed in a format that is similar to or the same as the format of the data that was used to train the model. Internal transformation are reversed in the model details and in the results of scoring.

Some of the attributes used by the model correspond to columns in the build data. However, because of logic specific to the algorithm, nested data, and transformations, some attributes donot correspond to columns.

For example, a nested column in the training data is not interpreted as an attribute by the model. During the model build, Oracle Data Mining explodes nested columns, and each row (an attribute name/value pair) becomes an attribute.

Some algorithms, for example Support Vector Machines (SVM) and Generalized Linear Models (GLM), only operate on numeric attributes. Any non-numeric column in the build data is exploded into binary attributes, one for each distinct value in the column (SVM). GLM does not generate a new attribute for the most frequent value in the original column. These binary attributes are set to one only if the column value for the case is equal to the value associated with the binary attribute.

Algorithms that generate coefficients present challenges in regards to interpretability of results. Examples are SVM and Non-Negative Matrix Factorization (NMF). These algorithms produce coefficients that are used in combination with the transformed attributes. The coefficients are relevant to the data on the transformed scale, not the original data scale.

For all these reasons, the attributes listed in the model details donot resemble the columns of data used to train the model. However, attributes that undergo embedded transformations, whether initiated by Automatic Data Preparation (ADP) or by a user-specified transformation list, appear in the model details in their pre-transformed state, as close as possible to the original column values. Although the attributes are transformed when they are used by the model, they are visible in the model details in a form that can be interpreted by a user.