Contents
What is shallow decision tree?
A shallow tree is a small tree (most of the cases it has a small depth). A full grown tree is a big tree (most of the cases it has a large depth).
What makes extra trees more random than regular random forests?
Random Forest chooses the optimum split while Extra Trees chooses it randomly. However, once the split points are selected, the two algorithms choose the best one between all the subset of features. Therefore, Extra Trees adds randomization but still has optimization.
Why do we want to use a small tree depth?
Larger the depth of the tree more are the chances of variance(overfitting). Whereas smaller the depth of the tree more are the chances of bias tree(underfitting).
Does decision tree need normalization?
Normalization should have no impact on the performance of a decision tree. It is generally useful, when you are solving a system of equations, least squares, etc, where you can have serious issues due to rounding errors.
What is decision tree in data science?
A decision tree is a largely used non-parametric effective machine learning modeling technique for regression and classification problems. To find solutions a decision tree makes sequential, hierarchical decision about the outcomes variable based on the predictor data.
What is decision tree training?
Decision tree learning is the construction of a decision tree from class-labeled training tuples. A decision tree is a flow-chart-like structure, where each internal (non-leaf) node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label.
What is decision tree machine learning?
Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves.