Can decision tree handle large data?
A set of decision trees are built in parallel on tractable size training data sets which are a subset of the original data. Results from cross validation experiments on a data set suggest this approach may be effectively applied to large sets of data.
Is decision tree affected by scale of data?
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.
What are some disadvantages that you see in decision trees when built for larger datasets?
The major limitations include:
- Inadequacy in applying regression and predicting continuous values.
- Possibility of spurious relationships.
- Unsuitability for estimation of tasks to predict values of a continuous attribute.
- Difficulty in representing functions such as parity or exponential size.
What is the weaknesses of decision tree?
Disadvantages of decision trees: They are unstable, meaning that a small change in the data can lead to a large change in the structure of the optimal decision tree. They are often relatively inaccurate. Many other predictors perform better with similar data.
How can you tell if a decision tree is accurate?
Accuracy can be computed by comparing actual test set values and predicted values. Well, you got a classification rate of 67.53%, considered as good accuracy. You can improve this accuracy by tuning the parameters in the Decision Tree Algorithm.
What is the maximum number of nodes in a decision tree?
A depth of 1 means 2 terminal nodes. Depth of 2 means max. 4 nodes. Depth of 3 means max. 8 nodes. The maximum number of terminal nodes in a tree is 2 to the power of the depth. For the examples in this chapter, I used the rpart R package that implements CART (classification and regression trees).
Why is max depth of decision tree too high?
For testing error, it gets less obvious. If you set max_depth too high, then the decision tree might simply overfit the training data without capturing useful patterns as we would like; this will cause testing error to increase.
When to stop running a decision tree algorithm?
The decision tree algorithms will continue running until a stop criteria such as the minimum number of observations etc. is reached. Once a decision tree is built, many nodes may represent outliers or noisy data. Tree pruning method is applied to remove unwanted data. This, in turn, improves the accuracy of the classification model.
Can we calculate the maximum training error for a decision tree?
For any dataset D, with the label space containing m labels. Using decision tree, can we calculate the maximum training error on that? Yes I have to admit, it’s a homework question and I have completely no clues on that. Could any one gives me some hint on that? And what is label, does it mean edges of the tree?