Which of the following machine learning algorithms typically finds a model by computing the information gain? Select one:
a. decision tree induction
b. k-th nearest neighbor
c. perceptron
d. support vector machines
The correct answer and explanation is:
The correct answer is a. decision tree induction.
Explanation:
Decision tree induction is a machine learning algorithm that uses information gain as a key criterion for constructing the decision tree. The process begins by evaluating all possible features and selecting the one that provides the highest information gain to split the data at each node.
Information gain measures the reduction in entropy, or uncertainty, after a dataset is divided into subsets based on a feature. Entropy is a measure of randomness or disorder, and a lower entropy indicates a more homogeneous set. By choosing the feature that maximizes information gain, the algorithm aims to create the most informative splits, thereby improving the efficiency and accuracy of the model.
This process is repeated recursively at each node of the tree, resulting in a structure where each path from the root to the leaves represents a series of decisions based on feature values that lead to predictions. Decision tree induction, in essence, constructs a flowchart-like model where each node represents a decision rule, and the branches correspond to outcomes based on different feature values.
In contrast:
- k-th nearest neighbor (k-NN) is a non-parametric method that does not build a model in the traditional sense. Instead, it classifies a data point based on the majority class of its k-nearest neighbors in the feature space.
- Perceptron is a linear classifier that adjusts its weights based on errors made in predicting binary class labels. It does not compute information gain.
- Support vector machines (SVM) work by finding the hyperplane that maximally separates data points of different classes, but they do not use information gain as part of their optimization process.
Thus, decision tree induction is the algorithm most closely associated with the concept of information gain.