  The goal of query performance prediction (QPP) is to automatically estimate
the effectiveness of a search result for any given query, without relevance
judgements. Post-retrieval features have been shown to be more effective for
this task while being more expensive to compute than pre-retrieval features.
Combining multiple post-retrieval features is even more effective, but
state-of-the-art QPP methods are impossible to interpret because of the
black-box nature of the employed machine learning models. However,
interpretation is useful for understanding the predictive model and providing
more answers about its behavior. Moreover, combining many post-retrieval
features is not applicable to real-world cases, since the query running time is
of utter importance. In this paper, we investigate a new framework for feature
selection in which the trained model explains well the prediction. We introduce
a step-wise (forward and backward) model selection approach where different
subsets of query features are used to fit different models from which the
system selects the best one. We evaluate our approach on four TREC collections
using standard QPP features. We also develop two QPP features to address the
issue of query-drift in the query feedback setting. We found that: (1) our
model based on a limited number of selected features is as good as more complex
models for QPP and better than non-selective models; (2) our model is more
efficient than complex models during inference time since it requires fewer
features; (3) the predictive model is readable and understandable; and (4) one
of our new QPP features is consistently selected across different collections,
proving its usefulness.
