PPT-Decision Tree Compared to Other Methods

How is normal Decision Tree different from Random Forest A Decision Tree is a supervised learning strategy in machine learning It may be used with both classification and regression algorithms As the name says it resembles a tree with nodes The branches are determined by the number of criteria It separates data into these branches until a threshold unit is reached . CS/. BioE. 598. Tandy Warnow. Alignment Error/Accuracy. SP-FN: percentage of homologies in the true alignment that are *not* recovered. SP-FP: percentage of homologies in the estimated alignment that are false. Shiqin Yan. Objective. Utilize the already existed database of the mushrooms to build a decision tree to assist the process of determine the whether the mushroom is . poisonous. .. DataSet. Existing record . Arko. Barman. With additions and modifications by Ch. . Eick. COSC 4335 Data Mining. Example of a Decision Tree. categorical. categorical. continuous. class. Refund. MarSt. TaxInc. YES. NO. NO. NO. Yes. SVM. Sindhu Kuchipudi. INSTRUCTOR Dr.DONGCHUL KIM. OUTLINE:. Introduction. Decision-tree-based SVM.. The class separability Measure in feature space.. The Improved Algorithm For Decision-tree- Based SVM.. Decision Tree. Advantages. Fast and easy to implement, Simple to understand. Modular, Re-usable. Can be learned .  . can be constructed dynamically from observations and actions in game, we will discuss this further in a future topic called ‘Learning’). CSE 335/435. Resources:. Main: . Artificial Intelligence: A Modern Approach (Russell and . Norvig. ; Chapter “Learning from Examples. ”). Alternatives:. http. ://www.dmi.unict.it/~. apulvirenti/agd/Qui86.pdf. Kai Müller. Tree searching: exhaustive search. branch addition algorithm. Branch. and bound. L. min. =L. (random tree). „search tree“ as in branch addition. at each level, if . L < L. min. . Decision Tree. Advantages. Fast and easy to implement, Simple to understand. Modular, Re-usable. Can be learned .  . can be constructed dynamically from observations and actions in game, we will discuss this further in a future topic called ‘Learning’). Questions can be of the form. 1) Explain/show property X. 2) Which properties are required for Y. 3) Apply method Z. 4) How do methods A and B compare. 5) I have problem C what methods are applicable?. Lecture 15: Decision Trees Outline Motivation Decision Trees Splitting criteria Stopping Conditions & Pruning Text Reading: Section 8.1, p. 303-314. 2 Geometry of Data Recall: l ogistic regression Decision Tree & Bootstrap Forest C. H. Alex Yu Park Ranger of National Bootstrap Forest What not regression? OLS regression is good for small-sample analysis. If you have an extremely large sample (e.g. Archival data), the power level may aproach 1 (.99999, but it cannot be 1). Increasing molecular evidence indicates that hybridization is far more . common in animals than has traditionally been recognized. . A. B. C. D. Hybridization. A. B. C. D. A. B. C. D. Non-. Introgressed. and Regress Decision Tree. KH Wong. Decision tree v3.(230403b). 1. We will learn : the Classification and Regression decision Tree ( CART) ( or . Decision Tree. ). Classification decision tree. uses. Folks often use the term “reliability” without a very clear definition of what it is. . Methods of assessing performance - Simulation.. . Enormous advantage that a large number of replicates can be examined, and this allows us to account for .