Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 18

There are multiple packages available in R to implement decision trees, such as ctree, rpart, and tree. Here is an example:

> library(rpart) > x <- cbind(x_train,y_train) # grow tree > fit <- rpart(y_train ~ ., data = x,method="class") > summary(fit) #Predict Output > predicted= predict(fit,x_test)

In the code above, y_train and x_train represent dependent and independent variables, respectively, and x represents training data. Similarly, in Python we have the following:

#Import Library #Import other necessary libraries like pandas, numpy… from sklearn import tree #Assumed you have, X (predictor) and Y (target) for training dataset and x_test(predictor) of test_dataset # Create tree object model = tree.DecisionTreeClassifier(criterion='gini') # for classification, here you can change the algorithm as gini or entropy (information gain) by default it is gini # model = tree.DecisionTreeRegressor() for regression # Train the model using the training sets and check score model.fit(X, y) model.score(X, y) #Predict Output predicted= model.predict(x_test)