left child and the ones that lack it will be passed to the right child. To split the left node, the process would be the same, except there would only be 3 samples to check. Sometimes a node may not need to be split at all if it is a pure set, where all samples at the node are just cancerous or non-cancerous. Splitting the node may lead to the tree being more inaccurate and in this case it will not be split. The tree would now achieve 100% accuracy if the samples that were used to build it are tested. This isn't a good idea, however, since the tree would overfit the data. The best course of action is to try testing the tree on other samples, of which are not part of the original set. Two outside samples are below: By following the tree, NC10 was classified correctly, but C15 was classified as NC. For other samples, this tree would not be 100% accurate anymore. It could be possible to improve this though, with options such as increasing the depth of the tree or increasing the size of the training set. Advantages Information gain is the basic criterion to decide whether a feature should be used to split a node or not. The feature with the optimal split i.e., the highest value of information gain at a node of a decision tree is used as the feature for splitting the node. The concept of information gain function falls under the C4.5 algorithm for generating the decision trees and selecting the optimal split for a decision tree node. Some of its advantages include: It can work with both continuous and discrete variables. Due to the factor –[p ∗ log(p)] in the entropy definition as given above, leaf nodes with a small number of instances are assigned less weight and it favors dividing rest of the data into bigger but homogeneous groups. And thus, as we dig deeper into the depth of the tree, the dataset becomes more homogeneous. This approach is usually more stable and chooses the most impactful features on the nodes. Drawbacks and Solutions Although information gain is usually a good measure for deciding the relevance of an attribute, it is not perfect. A notable problem occurs when information gain is applied to attributes that can take on a large number of distinct values. For example, suppose