Understanding Decision Trees and Random Forest Models

Nov 28, 2025

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We are going to deconstruct the Decision Tree functionality in this blog, explain why a Random Forest is better than most single-task models, and show what you can achieve by practicing them practically.

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Understanding Decision Trees and Random Forest Models

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Random Forest

Random Forest. Predrag Radenkovi ć 3237/10 Facult y of Electrical Engineering University Of Belgrade. Definition.

630 views • 14 slides

Decision Trees

Decision Trees. Prof. A.L. Yuille. Stat 231. Fall 2004. Duda, Hart & Stork. Chp 81-8.3 . Decision Trees. Decision Trees. Binary Classification Trees. Data can be non-metric, list of binary attributes. 2. Classification Decision Trees. Nonmetric. Data is list of binary attributes.

864 views • 15 slides

Decision Trees

Decision Trees. Example: Conducted survey to see what customers were interested in new model car Want to select customers for advertising campaign. training set. Basic Information Gain Computations. Result: I_Gain_Ratio: city>age>car. Result: I_Gain: age > car=city.

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Decision Trees

Decision Trees. Daisy Guevara Gilberto Jiménez Orlando Ruiz Jan Salomón. What is a rooted tree?. It is a tree in which one vertex has been designated as the root and every edge is directed away from the root. What are decision trees?.

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Decision Trees and Decision Tables

Decision Trees and Decision Tables. Decision Trees and Decision Tables. Often our problem solutions require decisions to be made according to two or more conditions or combinations of conditions Decision trees represent such decision as a sequence of steps

1.8k views • 39 slides

Decision Trees

Decision Trees. Highly used and successful Iteratively split the Data Set into subsets one attribute at a time, using most informative attributes first Continue until you can label each leaf node with a class Attribute Features – discrete/nominal (can extend to continuous features)

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Decision Trees

Decision Trees. Example. Example 2. Examples, which one is better?. Good when. Samples are attribute-value pairs Target function has discrete output values Disjunctions required Missing, noisy training data. Construction. Top down construction

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Decision Trees

Decision Trees. Shalev Ben-David. Definition. Given a function and oracle access to , determine f(x) with minimum number of queries E.g. f is OR on the bits of x – Grover search D(f) is the deterministic query complexity R(f) is the randomized query complexity

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Classification and Regression trees: CART BOOSTING AND BAGGING RANDOM FOREST

Classification and Regression trees: CART BOOSTING AND BAGGING RANDOM FOREST Data Mining Trees: ARF. Credit Card?. Car?. Age<30. N. Y. N. Y. Y. N. Reject. Approve. Reject. Approve. Tree methods: Dependent variable is categorical

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Decision Trees

Decision Trees. Many classes of problems can be formalized as search problems in decision trees . We are going to look at two different applications of search trees: First, we will solve the n-queens problem using backtracking search.

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Decision Trees

Decision Trees. Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting. Another Example Problem. A Decision Tree. Type. Car. SUV. Minivan. -. Doors. Tires. 2. 4. Blackwall. Whitewall. +. -. -. +. Decision Trees. Decision tree representation

1.44k views • 31 slides

Decision Trees

Decision Trees. Prof. A.L. Yuille. Stat 231. Fall 2004. Duda, Hart & Stork. Chp 81-8.3. Decision Trees. Decision Trees. Binary Classification Trees. Data can be non-metric, list of binary attributes. 2. Classification Decision Trees. Nonmetric. Data is list of binary attributes.

391 views • 15 slides

Decision Trees

Decision Trees. Greg Grudic (Notes borrowed from Thomas G. Dietterich and Tom Mitchell) Modified by Longin Jan Latecki Some slides by Piyush Rai. Outline. Decision Tree Representations ID3 and C4.5 learning algorithms (Quinlan 1986) CART learning algorithm (Breiman et al. 1985)

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DECISION TREES

DECISION TREES. An internal node represents a test on an attribute. A branch represents an outcome of the test, e.g., Color=red. A leaf node represents a class label or class label distribution.

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Decision Trees

Decision Trees. The “No Free Lunch” Theorem. Is there any representation that is compact (ie, sub-exponential in n) for all functions? Function = truth table n attributes  2^n rows in table Classification/target column is 2^n long

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Understanding the Growth and Decline of Forest Trees

Understanding the Growth and Decline of Forest Trees. Next Generation Science / Common Core Standards Addressed!.

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Decision trees

Decision trees. Decision trees. Originally invented for concept learning (two-class classification) over discrete features But later it was extended to more classes And also to continuous features (here: “attributes”) Also closely related to the learning of logical formulas

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Decision Trees

Decision Trees. Jianping Fan Dept of Computer Science UNC-Charlotte. The problem of Classification. Given a set of training samples and their attribute values (x) and labels (y), try to determine the labels y of new examples. Classifier Training y = f(x) Prediction y given x.

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Decision Trees

Decision Trees. categorical. categorical. continuous. class. Example of a Decision Tree. Splitting Attributes. Refund. Yes. No. NO. MarSt. Married. Single, Divorced. TaxInc. NO. < 80K. > 80K. YES. NO. Model: Decision Tree. Training Data. NO. Another Example of Decision Tree.

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