What is Information Gain? Information Gain Explained
Information gain is a measure used in decision tree algorithms and other machine learning algorithms to assess the relevance or importance of a feature in predicting or classifying a target variable. It quantifies how much information a particular feature provides about the target variable and is used to determine the order in which features are selected or split in the learning process.
The concept of this ML measure is derived from information theory and entropy. In information theory, entropy is a measure of the uncertainty or randomness in a random variable. In the context of machine learning, entropy is used to quantify the impurity or heterogeneity of a set of instances with respect to their class labels.
The information gain of a feature is calculated by comparing the entropy of the target variable before and after splitting the data based on that feature. The higher the information gain, the more useful or informative the feature is in reducing the uncertainty about the target variable.
Here’s how this gain is typically calculated:
Calculate the entropy of the original set: Compute the entropy of the target variable using the distribution of class labels in the entire dataset.
Split the data based on the feature: Divide the dataset into subsets based on the values of the selected feature.
Calculate the weighted average entropy of the subsets: Calculate the entropy of each subset (corresponding to a specific value of the feature) by considering the distribution of class labels within that subset. Weight each entropy value by the proportion of instances in that subset.
Calculate the information gain: Subtract the weighted average entropy of the subsets from the entropy of the original set. This yields the information gain associated with the selected feature.
This gain is typically used as a criterion to determine the best feature to split on in decision tree algorithms. The feature with the highest gain is chosen as the root or the next node in the tree. This process is recursively applied to the child nodes until a stopping criterion is met or a leaf node (terminal decision) is reached.
By selecting features with high information gain, decision tree algorithms can efficiently partition the data based on the most relevant attributes and make accurate predictions or classifications.
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