Machine learning algorithms are commonly utilized for independent observations and may not be efficient for analyzing spatial data sets since dependency is present in all directions‎. ‎In spatial data sets‎, ‎due to spatial correlations between observations‎, ‎the independence assumption is violated; thus‎, ‎some modifications are necessary for accurate analysis‎. ‎In this paper‎, ‎the well-known CART algorithm is adapted to the spatial domain by proposing two new approaches namely kernel-based weighted CART and kernel-based weighted CART with spatially split rules‎. ‎The first approach suggests kernel-based weights as a new method for weighting the observations according to their spatial locations‎. ‎The weights are inversely proportional to the density of the data location‎. ‎We also use the Voronoi and Kriging weights; all of which assign less weight to clustered data than others‎. ‎The second approach is defined based on spatial entropy as an impurity criterion in the weighted CART to achieve a more accurate algorithm‎. ‎Findings are evaluated by conducting simulation studies and analyzing a real data set to highlight the advantages and limitations of the proposed approaches‎. ‎The numerical results indicate that the proposed W-CART-SSR algorithm with class-based kernel weight outperforms other algorithms in terms of accuracy‎, ‎tree structure‎, ‎and implementation time‎.

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