Notes:
Decision trees are a type of machine learning algorithm that can be used to classify or predict the outcome of certain events. They are called decision trees because they consist of a series of branching paths, each of which represents a different decision or outcome. At each branching point, the decision tree uses a set of rules or criteria to determine which path to follow based on the input data.
Decision trees can be used in a variety of applications, including dialog systems. In this context, a decision tree can be used to determine the appropriate response to a user’s input in a conversation. For example, a decision tree might be used to identify the topic of a user’s message, and then select an appropriate response from a pre-defined set of options based on the identified topic. This allows the dialog system to generate more natural and appropriate responses to the user, and to adapt to changes in the conversation.
See also:
Decision Tree & Dialog Systems 2019
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- Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
- Decision Tree 1: how it works
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- Let’s Write a Decision Tree Classifier from Scratch – Machine Learning Recipes #8
- Decision Analysis 3: Decision Trees
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- Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
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- TreePlan and Decision Tree Analysis in Excel
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- Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn
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- How decision trees algorithm works
- C4.5 algorithm and Multivariate Decision Trees
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- Tutorial 37: Entropy In Decision Tree Intuition
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- Computer Troubleshooting & Tech Support : How to Draw a Decision Tree in Excel
- How Decision Trees Work 1/2 .. an Introduction + What is Entropy
- R – Classification Trees (part 2 using rpart)
- The Decision Tree: Going GLOBAL Doesn’t Have to Be Complicated.
- Create Interactive Decision Tree Troubleshooters with Zingtree
- Introduction to Decision Trees
- Decision Trees – Example 1
- Coding A Decision Tree – Intro to Machine Learning
- Decision Tree Parameters – Intro to Machine Learning
- Building Decision Tree Models using RapidMiner Studio
- Using the TreePlan Add-In for Excel
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- Constructing a Decision Tree First Split – Intro to Machine Learning
- Lecture 21: Regression Trees
- Constructing A Decision Tree/Third Split – Intro to Machine Learning
- Decision Tree Accuracy – Intro to Machine Learning
- Constructing a Decision Tree 2nd Split – Intro to Machine Learning
- Decision Tree Accuracy – Intro to Machine Learning
- Introduction to Decision Trees in Excel (TreePlan Add-In)
- Decision Trees Learning – Georgia Tech – Machine Learning
- Decision Trees Continuous Attributes – Georgia Tech – Machine Learning
- Creating a ‘Decision Tree’ Model | RapidMiner
- How to Draw a Decision Tree
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- undergraduate machine learning 31: Decision trees
- PrecisionTree Tutorial 1 – Basics – Part1
- Creating Simple Decision Tree with Add-in
- Decision Tree Expressiveness Quiz – Georgia Tech – Machine Learning
- Decision Tree Expressiveness Quiz 2 – Georgia Tech – Machine Learning
- JMP 12 Tutorials – Decision Tree JMP
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- WEKA Tutorial Video – Decision Trees – Classification Model
- Decision Tree Confusion Matrix – Intro to Machine Learning
- C4.5 Decision Tree implementation in hadoop (IIT G)
- Constructing a Decision Tree First Split – Intro to Machine Learning
- Coding A Decision Tree – Intro to Machine Learning
- Introduction to common terminology in Decision Tree
- Constructing A Decision Tree/Third Split – Intro to Machine Learning
- Decision Tree Accuracy – Intro to Machine Learning
- WEKA – Missing values, Decision Tree, Confusion Matrix, Numeric to Nominal
- Constructing a Decision Tree 2nd Split – Intro to Machine Learning
- Decision Tree Accuracy – Intro to Machine Learning
- Decision Tree Parameters – Intro to Machine Learning
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- Decision Tree Expressiveness – Georgia Tech – Machine Learning
- Decision Trees and Excel: The IF Function
- Decision Trees Expressiveness OR – Georgia Tech – Machine Learning
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- PrecisionTree Quick Start – Step 1: Plan the decision tree model
- Postgame Poker Analysis Using Decision Tree – Will Tipton