Contents
How can decision trees be reduced?
Post-pruning (or just pruning) is the most common way of simplifying trees. Here, nodes and subtrees are replaced with leaves to reduce complexity. Pruning can not only significantly reduce the size but also improve the classification accuracy of unseen objects.
How do decision trees handle missing values?
There are several methods used by various decision trees. Simply ignoring the missing values (like ID3 and other old algorithms does) or treating the missing values as another category (in case of a nominal feature) are not real handling missing values.
Can a decision tree handle non linear data?
Decision trees is a non-linear classifier like the neural networks, etc. It is generally used for classifying non-linearly separable data. Even when you consider the regression example, decision tree is non-linear.
How can we avoid overfitting in a decision tree?
Two approaches to avoiding overfitting are distinguished: pre-pruning (generating a tree with fewer branches than would otherwise be the case) and post-pruning (generating a tree in full and then removing parts of it). Results are given for pre-pruning using either a size or a maximum depth cutoff.
Can decision tree handle null values?
For this reason, decision trees are robust to outliers. Null values: You have to replace them (unless the software you use already does that for you, which is not generally the case).
Which is the best way to tune a decision tree?
The best way to tune this is to plot the decision tree and look into the gini index. Interpreting a decision tree should be fairly easy if you have the domain knowledge on the dataset you are working with because a leaf node will have 0 gini index because it is pure, meaning all the samples belong to one class.
How to avoid overfitting in decision tree learners?
Decision-tree learners can create over-complex trees that do not generalise the data well. This is called overfitting. Mechanisms such as pruning, setting the minimum number of samples required at a leaf node or setting the maximum depth of the tree are necessary to avoid this problem.
What are the disadvantages of a decision tree?
The disadvantages of decision trees include: Decision-tree learners can create over-complex trees that do not generalise the data well. This is called overfitting. Decision trees can be unstable because small variations in the data might result in a completely different tree being generated.
How to tune a decision tree using hyperparameter?
According to the paper, An empirical study on hyperparameter tuning of decision trees the ideal min_samples_split values tend to be between 1 to 40 for the CART algorithm which is the algorithm implemented in scikit-learn. min_samples_split is used to control over-fitting.
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