Pruning should reduce the size of a learning tree without reducing predictive accuracy as.
mentioned idea, we can formulate the problem of tree pruning via max-heap projection as the optimization problembelow: min w;b L(y;F(x 0;x 1;;x p;w;b))+ kwk 1 s:t:w 2P;where istheregularizationparametercontrollingthe sparsityofw,Lisagenerallossfunctionwithrespect to x i, w and b, and prediction function F takes the followingform: F(x 0;x 1;;x p;w;b) = b1 n + Xp.
Tree Data Structures for N-Body Simulation Nonparametric Probability Density Estimation (Richard A. Tapia and James R. Thompson) Recently Searched.
Linear Schrödinger Equation with an Almost Periodic Potential C0 Interior Penalty Methods for an Elliptic Distributed Optimal Control Problem on Nonconvex. Relaxation of Non-Convex Variational Problems (II) Spectral Analysis of a Preconditioned Iterative Method for the Convection‐Diffusion Equation.
It can be seen that 1) the performance of stability selection is quite stable; (2) the models selected by stability selection perform well on the testing set, which is comparable or even better than the performance of the models selected by cross validation.
-"Pruning Decision Trees via Max-Heap Projection". The decision tree model has gained great popularity both in academia and industry due to its capability of learning highly non-linear decision boundaries, and at the same time, still preserving interpretability that usually translates into transparency of decision-making.
However, it has been a longstanding challenge for learning robust decision tree models since the learning process is.