What is a shallow decision tree?

What is a 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 is the difference between decision trees and random forest?

A decision tree is built on an entire dataset, using all the features/variables of interest, whereas a random forest randomly selects observations/rows and specific features/variables to build multiple decision trees from and then averages the results.

How a decision tree is learned?

Decision tree learning is a method commonly used in data mining. A tree is built by splitting the source set, constituting the root node of the tree, into subsets—which constitute the successor children. The splitting is based on a set of splitting rules based on classification features.

What is the difference between ” fully developed decision trees “?

Considering our shallow tree we can say that the model has low variance since changing the sample does not change too much the model. It needs too many changed data points to be considered unstable. At the same time we can say that has a high bias, since it really can’t represent the sine function which is the true model.

How can we tune the decision trees to make a workaround?

How can we tune the decision trees to make a workaround? This is something which is planned to be done. Setting the minimum number of samples required at a leaf node or a split as well as setting the maximum depth of the tree are how you want to work around this.

Can a single point change the shape of a fitted tree?

In some regions at least, a single point sampled differently can change the shape of the fitted tree. As a general rule of thumb when a model has low bias it has also high variance and when it has low variance it has high bias. This is not true always, but it happens very often. And intuitively is a correct idea.