Coursera Machine Learning Techniques Course Note 09-decision Tree

This is what we have learned (except decision tree)

Here is a typical decision tree algorithm, with four places to choose from:

Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is that the data are divided into two categories, the purity of these two types of data (purifying).

The following is a measure of purity:

Finally, when to stop:

Decision tree may be overfitting, reducing the number of Ein and leaves (indicating the complexity of the tree)

If a feature is missing, an alternative feature can be found:

Compare the cart to the AdaBoost: The AdaBoost is cut across the plane, and the cart is cut in the cut plane (conditional cutting):

This lesson teacher is more general, in detail can refer to statistical learning methods.

Coursera Machine Learning Techniques Course Note 09-decision Tree