Contents
What is caret package?
The caret package (short for Classification And REgression Training) contains functions to streamline the model training process for complex regression and classification problems. caret loads packages as needed and assumes that they are installed. If a modeling package is missing, there is a prompt to install it.
What is the package used in implementation of decision tree?
R has packages which are used to create and visualize decision trees. For new set of predictor variable, we use this model to arrive at a decision on the category (yes/No, spam/not spam) of the data. The R package “party” is used to create decision trees.
What are the methods in the caret package?
7.0.31Partial Least Squares 7.0.32Patient Rule Induction Method 7.0.33Polynomial Model 7.0.34Prototype Models 7.0.35Quantile Regression 7.0.36Radial Basis Function 7.0.37Random Forest 7.0.38Regularization 7.0.39Relevance Vector Machines 7.0.40Ridge Regression 7.0.41Robust Methods 7.0.42Robust Model 7.0.43ROC Curves 7.0.44Rule-Based Model
How to create a decision tree in R?
I tried implementing a decision tree in the R programming language using the caret package. trctrl <- trainControl (method = “repeatedcv”, number = 10, repeats = 3) set.seed (3333) dtree_fit <- train (V7 ~., data = training, method = “rpart”, trControl = trctrl, tuneLength = 10) dtree_fit$finalModel
How does the algorithm of a decision tree model work?
In this chapter we’ll describe the basics of tree models and provide R codes to compute classification and regression trees. The algorithm of decision tree models works by repeatedly partitioning the data into multiple sub-spaces, so that the outcomes in each final sub-space is as homogeneous as possible.
Where are the decision nodes in a cart model?
The resulting tree is composed of decision nodes, branches and leaf nodes. The tree is placed from upside to down, so the root is at the top and leaves indicating the outcome is put at the bottom. Each decision node corresponds to a single input predictor variable and a split cutoff on that variable.