How do decision trees decide the value to be split upon for continous variables?
37646
How regression trees split, when all the Features and target have only continuous values
9080
What is the benefit of breaking up a continuous predictor variable?
68137
Independence and conditional independence between random variables
49877
Expected value and indicator random variable
110100
logic behind weighted KNN
113178
How is the optimal probability cut-off in a ROC defined by the R package Epi?
86854
Critical Value for Hypothesis testing
62549
Chain Rule Intuition
75831
Inconsistency in divide-by-zero behavior between different value types
103456
Non-perpendicular hyperplane decision trees
41200
Understanding Naive Bayes
115126
What is a loss function in decision theory?
121252
Interpreting output of importance of a random forest object in R
151799
Decision tree not using all features from training dataset
107491
What happens if the explanatory and response variables are sorted independently before regression?
58047
Decision Trees and Regression - Can predicted values be outside range of training data?
139294
Difference in implementation of binary splits in decision trees