This chapter describes Minimum Description Length, the supervised technique for calculating attribute importance.

This chapter includes the following topics:

Minimum Description Length (MDL) is an information theoretic model selection principle. MDL assumes that the simplest, most compact representation of data is the best and most probable explanation of the data. The MDL principle is used to build Oracle Data Mining attribute importance models.

MDL considers each attribute as a simple predictive model of the target class. These single predictor models are compared and ranked with respect to the MDL metric (compression in bits). MDL penalizes model complexity to avoid over-fit. It is a principled approach that takes into account the complexity of the predictors (as models) to make the comparisons fair.

With MDL, the model selection problem is treated as a communication problem. There is a sender, a receiver, and data to be transmitted. For classification models, the data to be transmitted is a model and the sequence of target class values in the training data.

Attribute importance uses a two-part code to transmit the data. The first part (preamble) transmits the model. The parameters of the model are the target probabilities associated with each value of the prediction. For a target with j values and a predictor with k values, n_{i} (i= 1,..., k) rows per value, there are C_{i}, the combination of j-1 things taken n_{i}-1 at time possible conditional probabilities. The size of the preamble in bits can be shown to be Sum(log_{2}(C_{i})), where the sum is taken over k. Computations like this represent the penalties associated with each single prediction model. The second part of the code transmits the target values using the model.

It is well known that the most compact encoding of a sequence is the encoding that best matches the probability of the symbols (target class values). Thus, the model that assigns the highest probability to the sequence has the smallest target class value transmission cost. In bits this is the Sum(log_{2}(p_{i})), where the p_{i} are the predicted probabilities for row _{i} associated with the model.

The predictor rank is the position in the list of associated description lengths, smallest first.

Automatic Data Preparation performs supervised binning for MDL. Supervised binning uses decision trees to create the optimal bin boundaries. Both categorical and numerical attributes are binned.

MDL handles missing values naturally as missing at random. The algorithm replaces sparse numerical data with zeros and sparse categorical data with zero vectors. Missing values in nested columns are interpreted as sparse. Missing values in columns with simple data types are interpreted as missing at random.

If you choose to manage your own data preparation, keep in mind that MDL usually benefits from binning. However, the discriminating power of an attribute importance model can be significantly reduced when there are outliers in the data and external equal-width binning is used. This technique can cause most of the data to concentrate in a few bins (a single bin in extreme cases). In this case, quantile binning is a better solution.

See Also:

Chapter 19, "Automatic and Embedded Data Preparation"

Oracle Data Mining Application Developer's Guide for information about nested data and missing values