What is cp in CART?
The complexity parameter (cp) is used to control the size of the decision tree and to select the optimal tree size.
What is a cart model in R?
So, it is also known as Classification and Regression Trees (CART). Note that the R implementation of the CART algorithm is called RPART (Recursive Partitioning And Regression Trees) available in a package of the same name.
How do I reduce overfitting in random forest?
To avoid over-fitting in random forest, the main thing you need to do is optimize a tuning parameter that governs the number of features that are randomly chosen to grow each tree from the bootstrapped data.
Why is 100% accuracy on test data is not good?
This means that it is impossible that you have 100 accuracy because the nature you are going to model is not a function. If I want to explain it again there are two situations. first, the nature of the distribution is time variant or invariant.
How are training accuracy and testing accuracy different?
There are two issues about the problem, training accuracy and testing accuracy are significantly different. Overfitting of the model to the training data. Since you apply cross-validation, it seems that you should think about another solution.
How did the deep learning model achieve 100% accuracy?
There are rules of thumb while implementing a CNN model, you can let the model overfit the data and later implement regularization methods if the model overfits the data. I have deliberately added Dropout to make the model’s training process tougher, which makes the model jump close to 100% accuracy later than expected.
Is a 100% model accuracy on out of sample data overfitting?
I know that when a model is overfit, it performs well on data from its own trainingset but badly on out-of-sample data. To train and test my model I have shuffled and split the complete dataset into 70% for training and 30% for testing. My question: I am getting a 100% accuracy out of the prediction done on the testing set. Is this bad?
https://www.youtube.com/watch?v=H9wYemw-ZAI