{"id":1654,"date":"2019-04-24T05:45:49","date_gmt":"2019-04-24T05:45:49","guid":{"rendered":"http:\/\/kusuaks7\/?p=1259"},"modified":"2023-08-24T07:51:19","modified_gmt":"2023-08-24T07:51:19","slug":"decision-trees-how-to-optimize-my-decision-making-process","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/decision-trees-how-to-optimize-my-decision-making-process\/","title":{"rendered":"Decision Trees: How to Optimize My Decision Making Process?"},"content":{"rendered":"

Let's imagine you are playing a game of Twenty Questions<\/a>. Your opponent has secretly chosen a subject, and you must figure out what he\/she chose. At each turn, you may ask a yes-or-no question, and your opponent must answer truthfully. How do you find out the secret in the fewest number of questions?<\/p>\n

It should be obvious some questions are better than others. For example, asking "Can it fly?" as your first question is likely to be unfruitful, whereas asking "Is it alive?" is a bit more useful. Intuitively, you want each question to significantly narrow down the space of possibly secrets, eventually leading to your answer.<\/p>\n

That is the basic idea behind decision trees<\/strong>. At each point, you consider a set of questions that can partition your data set. You choose the question that provides the best split and again find the best questions for the partitions. You stop once all the points you are considering are of the same class. Then the task of classification is easy. You can simply grab a point, and chuck it down the tree. The questions will guide it to its appropriate class.<\/p>\n

IMPORTANT TERMS<\/strong><\/h3>\n

Decision tree is a type of supervised learning algorithm that can be used in both regression and classification problems. It works for both categorical and continuous input and output variables.<\/p>\n

\"decision-tree.png\"<\/p>\n

Let’s identify important terminologies on Decision Tree, looking at the image above:<\/p>\n