Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods. In this paper, we work out in full details this connection. In particular, we show that by slightly modifying their definition, random forests can be rewrit-ten as kernel methods (called KeRF for Kernel based on Random Forests) which are more interpretable and easier to analyze. Explicit expressions of KeRF estimates for some specific random forest models are given, together with upper bounds on their rate of consistency. We also show empirically that KeRF estimates compare favourably to random forest estimates.