Random forests are one of the most popular machine learning methods due to their accuracy and variable importance assessment. However, random only provide in a global sense. There is an increasing need for such assessments at local level, motivated by applications personalized medicine, policy-making, bioinformatics. We propose new nonparametric estimator that pairs flexible forest kernel with ...