Variable importance, interaction measures, and partial dependence plots are important summaries in the interpretation of statistical machine learning models. In this article, we describe new visualization techniques for exploring these model summaries. We construct heatmap graph-based displays showing variable importance jointly, which carefully designed to highlight aspects fit. a matrix-type ...

We consider factor analysis when we assume the distribution form is known up to its mean and variance. A prior is placed on the mean and variance and then inference is made as to whether or not any latent factors exist. Inference is carried out by comparing the concentrations of the prior and posterior about various subsets of the parameter space that are specified by hypothesizing factor struc...

At present, variable selection turns to prominence since it obviously alleviate a trouble of measuring multiple variables per sample. The partial least squares regression (PLS-R) and the score of Variable Importance in Projection (VIP) are combined together for variable selection. The value of VIP score which is greater than 1 is the typical rule for selecting relevant variables. Due to a const...

Abstract This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions. Methods such as variable importance measures proposed random forests, partial dependence plots, individual conditional expectation plots remain popular because they are both model-agnostic depend only on pre-trained model output, making them computationally effici...

In many application domains we need to find solutions that satisfy, apart from a set of hard constraints, a set of user defined preferences. Ceteris Paribus (CP)-networks have been proposed as an intuitively appealing framework for expressing preference statements. CP-nets have been further extended to incorporate information on the relative importance of the variables, resulting in a formalism...

Variable importance evaluation functions can be separated into two groups: those that use the model information and those that do not. The advantage of using a model–based approach is that is more closely tied to the model performance and that it may be able to incorporate the correlation structure between the predictors into the importance calculation. Regardless of how the importance is calcu...