Contents
How do you interpret a decision tree in Rapidminer?
A decision tree is a tree like collection of nodes intended to create a decision on values affiliation to a class or an estimate of a numerical target value. Each node represents a splitting rule for one specific Attribute.
Is decision tree scalable?
Decision trees and ensemble methods do not require feature scaling to be performed as they are not sensitive to the the variance in the data.
How do I know if my RapidMiner is accurate?
The accuracy is calculated by taking the percentage of correct predictions over the total number of examples. Correct prediction means the examples where the value of the prediction attribute is equal to the value of label attribute.
What does Kappa mean in RapidMiner?
Kappa = 0, all predictions are the majority class. Kappa = 1, all predictions are correct. Kappa = -1, all predictions are wrong. If you know only know accuracy is 99%, you don’t really know much. Because you might have a dataset with 9900 negative and only 100 positive example.
What is cross validation RapidMiner?
The cross validation allows you to check your models performance on one dataset which you use for training and testing. The cross validation splits your data into pieces. Similar to a split validation it trains on one part and then tests on the other.
How does a decision tree model work in RapidMiner?
The decision tree model is delivered from this output port. The ExampleSet that was given as input is passed without changing to the output through this port. An ExampleSet containing Attributes and weight values, where each weight represents the feature importance for the given Attribute.
How to create a weight based decision tree?
Decision Tree (Weight-Based) (RapidMiner Studio Core) Synopsis This operator generates a pruned decision tree based on an arbitrary attribute relevance test. The attribute weighting scheme should be provided as inner operator. This operator can be applied only on ExampleSets with nominal data.
How does the random forest operator work in RapidMiner?
It only works with ExampleSets with nominal Attributes. The Random Forest Operator creates several random trees on different Example subsets. The resulting model is based on voting of all these trees. Due to this difference, it is less prone to overtraining.
How is polynominal data set used in RapidMiner?
The ‘Polynominal’ data set with a numerical target Attribute is used as a label. Before training the model the data set is split into a training and a test set. Afterwards the regressed values are compared with the label values to obtain a performance measure using the Performance (Regression) Operator.