Decision trees in algorithms.
You are an electrical engineer designing a new integrated circuit involving potentially millions of components. How would you use graph theory to organize how many layers your chip must have to handle all of the interconnections? Which properties of graphs come into play in such a circumstance?
Trees occur in various venues in computer science: decision trees in algorithms, search trees, and so on. In linguistics, one encounters trees as well, typically as parse trees, which are essentially sentence diagrams, such as those you might have had to do in primary school, breaking a natural-language sentence into its components—clauses, subclauses, nouns, verbs, adverbs, adjectives, prepositions, and so on. What might be the significance of the depth and breadth of a parse tree relative to the sentence it represents? If you need to, look up parse tree and natural language processing on the Internet to see some examples.
Sample Solution
The decision to make strategic splits has a significant impact on a tree’s accuracy. The decision criteria for classification and regression trees are different. To decide whether to break a node into two or more sub-nodes, decision trees employ a variety of techniques. The homogeneity of the generated sub-nodes improves with the generation of sub-nodes. To put it another way, the purity of the node improves as the target variable grows. The decision tree divides the nodes into sub-nodes based on all available variables, then chooses the split that produces the most homogeneous sub-nodes. The type of target variables is also taken into account when choosing an algorithm. Let’s have a look at some of the algorithms that are employed in Decision Trees:
