2021년 2월 17일 (수) 08:07에 Pythagoras0님의 편집

2021-02-17T08:07:10Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:17:00Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T10:58:18Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q831366 Q831366]
===말뭉치===
# This Decision Tree may be used as a tool to construct or test such a policy for your organisation.<ref name="ref_a988f2d5">[https://www.dpconline.org/handbook/organisational-activities/decision-tree Digital Preservation Handbook]</ref>
# In psychology, the decision tree methods were used to model the human concept of learning.<ref name="ref_7135c010">[https://www.explorium.ai/blog/the-complete-guide-to-decision-trees/ Decision Trees: The Complete Guide to Decision Tree Classifier l Explorium]</ref>
# There is no more logical data to learn via decision tree classifier, than … tree classifications.<ref name="ref_7135c010" />
# Sometimes, it is very useful to visualize the final decision tree classifier model.<ref name="ref_7135c010" />
# Python supports various decision tree classifier visualization options, but only two of them are really popular.<ref name="ref_7135c010" />
# Decision tree software is used in data mining to simplify complex strategic challenges and evaluate the cost-effectiveness of research and business decisions.<ref name="ref_4038695f">[https://whatis.techtarget.com/definition/decision-tree Definition from WhatIs.com]</ref>
# In this paper, we present fundamental theorems for the instability problem of decision tree classifiers.<ref name="ref_e10ada1b">[https://dl.acm.org/doi/10.1145/775047.775131 Instability of decision tree classification algorithms]</ref>
# As per Wikipedia, A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.<ref name="ref_0e78b4fe">[https://datascience.foundation/sciencewhitepaper/understanding-decision-trees-with-python Understanding Decision Trees with Python]</ref>
# Generally, a decision tree is drawn upside down with its root at the top (recommended) and it is known as Top-Down Approach.<ref name="ref_0e78b4fe" />
# A sub section of the decision tree is called branch or sub-tree.<ref name="ref_0e78b4fe" />
# Its formula is: Entropy –Another very popular way to split nodes in the decision tree is Entropy.<ref name="ref_0e78b4fe" />
# A decision tree helps to decide whether the net gain from a decision is worthwhile.<ref name="ref_b3464e5b">[https://www.tutor2u.net/business/reference/decision-trees Decision Trees]</ref>
# Let's look at an example of how a decision tree is constructed.<ref name="ref_b3464e5b" />
# A decision tree starts with a decision to be made and the options that can be taken.<ref name="ref_b3464e5b" />
# A decision tree is a branched flowchart showing multiple pathways for potential decisions and outcomes.<ref name="ref_a661ed5f">[https://courses.lumenlearning.com/wm-principlesofmanagement/chapter/using-a-decision-tree/ Principles of Management]</ref>
# Even in only this simple form, a decision tree is useful to show the possibilities for a decision.<ref name="ref_a661ed5f" />
# A decision tree is a supervised learning technique that has a pre-defined target variable and is most often used in classification problems.<ref name="ref_6bf20a1d">[https://deepai.org/machine-learning-glossary-and-terms/decision-tree Decision Tree]</ref>
# A decision tree is a diagram or chart that people use to determine a course of action or show a statistical probability.<ref name="ref_370b9492">[https://www.investopedia.com/terms/d/decision-tree.asp Decision Tree Definition]</ref>
# Each branch of the decision tree represents a possible decision, outcome, or reaction.<ref name="ref_370b9492" />
# A decision tree is a graphical depiction of a decision and every potential outcome or result of making that decision.<ref name="ref_370b9492" />
# In the decision tree, each end result has an assigned risk and reward weight or number.<ref name="ref_370b9492" />
# The third experiment evaluates the accuracy of a selected tree compared to a randomly chosen decision tree.<ref name="ref_43fe7429">[https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0186-3 Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification]</ref>
# For calculating the semantic similarity and choosing the most accurate decision tree, we run the decision trees over the development set that is 10% of the dataset.<ref name="ref_43fe7429" />
# We expect the single-tree approach to yield shorter classification times than the ensemble due to the fact that there is no need to run all decision tree models over the testing data.<ref name="ref_43fe7429" />
# In this way, although each induced decision tree sees only part of the trained dataset the voting combines their predictions over the testing dataset.<ref name="ref_43fe7429" />
# A decision tree is a support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences.<ref name="ref_475596d9">[https://corporatefinanceinstitute.com/resources/knowledge/other/decision-tree/ Overview, Decision Types, Applications]</ref>
# A small change in the data can result in a major change in the structure of the decision tree, which can convey a different result from what users will get in a normal event.<ref name="ref_475596d9" />
# A decision tree is a popular method of creating and visualizing predictive models and algorithms.<ref name="ref_e18b3d12">[https://www.aunalytics.com/decision-trees-an-overview/ Decision Trees: An Overview]</ref>
# The basic goal of a decision tree is to split a population of data into smaller segments.<ref name="ref_e18b3d12" />
# Since this data was not used to train the model, it will show whether or not the decision tree has overlearned the training data.<ref name="ref_e18b3d12" />
# A decision tree is created for each subset, and the results of each tree are combined.<ref name="ref_e18b3d12" />
# The decision tree is a greedy algorithm that performs a recursive binary partitioning of the feature space.<ref name="ref_9442c955">[https://spark.apache.org/docs/1.3.0/mllib-decision-tree.html Spark 1.3.0 Documentation]</ref>
# Implementation details: For faster processing, the decision tree algorithm collects statistics about groups of nodes to split (rather than 1 node at a time).<ref name="ref_9442c955" />
# subsamplingRate : Fraction of the training data used for learning the decision tree.<ref name="ref_9442c955" />
# A decision tree is a simple representation for classifying examples.<ref name="ref_ca92485b">[https://artint.info/html/ArtInt_177.html foundations of computational agents -- 7.3.1 Learning Decision Trees]</ref>
# A decision tree or a classification tree is a tree in which each internal (non-leaf) node is labeled with an input feature.<ref name="ref_ca92485b" />
# Each decision tree can be used to classify examples according to the user's action.<ref name="ref_ca92485b" />
# A deterministic decision tree, in which all of the leaves are classes, can be mapped into a set of rules, with each leaf of the tree corresponding to a rule.<ref name="ref_ca92485b" />
# The Decision Tree algorithm, like Naive Bayes, is based on conditional probabilities.<ref name="ref_e0e16662">[https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/algo_decisiontree.htm Decision Tree]</ref>
# Decision Tree Rules Oracle Data Mining supports several algorithms that provide rules.<ref name="ref_e0e16662" />
# Figure 11-1 shows a rule generated by a Decision Tree model.<ref name="ref_e0e16662" />
# This rule comes from a decision tree that predicts the probability that customers will increase spending if given a loyalty card.<ref name="ref_e0e16662" />
# This decision tree describes how to use the alt attribute of the <img> element in various situations.<ref name="ref_45cc8300">[https://www.w3.org/WAI/tutorials/images/decision-tree/ An alt Decision Tree • Images • WAI Web Accessibility Tutorials]</ref>
# This decision tree does not cover all cases.<ref name="ref_45cc8300" />
# then it called a Categorical variable decision tree.<ref name="ref_e00217fe">[https://www.kdnuggets.com/2020/01/decision-tree-algorithm-explained.html Decision Tree Algorithm, Explained]</ref>
# Now, as we know this is an important variable, then we can build a decision tree to predict customer income based on occupation, product, and various other variables.<ref name="ref_e00217fe" />
# The primary challenge in the decision tree implementation is to identify which attributes do we need to consider as the root node and each level.<ref name="ref_e00217fe" />
# If there is no limit set on a decision tree, it will give you 100% accuracy on the training data set because in the worse case it will end up making 1 leaf for each observation.<ref name="ref_e00217fe" />
# It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed.<ref name="ref_8153d365">[https://www.saedsayad.com/decision_tree.htm Decision Tree]</ref>
# ID3 uses Entropy and Information Gain to construct a decision tree.<ref name="ref_8153d365" />
# A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogenous).<ref name="ref_8153d365" />
# A decision tree is one of the supervised machine learning algorithms.<ref name="ref_8aa6a8ab">[https://towardsai.net/p/programming/decision-trees-explained-with-a-practical-example-fe47872d3b53 Decision Trees Explained With a Practical Example]</ref>
# A part of the entire decision tree is called a branch or sub-tree.<ref name="ref_8aa6a8ab" />
# : This is the end of the decision tree where it cannot be split into further sub-nodes.<ref name="ref_8aa6a8ab" />
# The small variation in the input data can result in a different decision tree.<ref name="ref_8aa6a8ab" />
# It is a tree-structured classifier, where and In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node.<ref name="ref_3f022d5f">[https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm Machine Learning Decision Tree Classification Algorithm]</ref>
# Note: A decision tree can contain categorical data (YES/NO) as well as numeric data.<ref name="ref_3f022d5f" />
# The logic behind the decision tree can be easily understood because it shows a tree-like structure.<ref name="ref_3f022d5f" />
# Root node is from where the decision tree starts.<ref name="ref_3f022d5f" />
# A decision tree is a tree like collection of nodes intended to create a decision on values affiliation to a class or an estimate of a numerical target value.<ref name="ref_47d4d34c">[https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/trees/parallel_decision_tree.html RapidMiner Documentation]</ref>
# After generation, the decision tree model can be applied to new Examples using the Apply Model Operator.<ref name="ref_47d4d34c" />
# The CHAID Operator provides a pruned decision tree that uses chi-squared based criterion instead of information gain or gain ratio criteria.<ref name="ref_47d4d34c" />
# The ID3 Operator provides a basic implementation of unpruned decision tree.<ref name="ref_47d4d34c" />
# In rpart decision tree library, you can control the parameters using the rpart.control() function.<ref name="ref_6f8d8337">[https://www.guru99.com/r-decision-trees.html Decision Tree in R | Classification Tree & Code in R with Example]</ref>
# In its simplest form, a decision tree is a type of flowchart that shows a clear pathway to a decision.<ref name="ref_3481ab5d">[https://careerfoundry.com/en/blog/data-analytics/what-is-a-decision-tree/ What Is a Decision Tree and How Is It Used?]</ref>
# Luckily, a lot of decision tree terminology follows the tree analogy, which makes it much easier to remember!<ref name="ref_3481ab5d" />
# By including options for what to do in the event of not being hungry, we’ve overcomplicated our decision tree.<ref name="ref_3481ab5d" />
# A decision tree is a tree-structured classification model, which is easy to understand, even by nonexpert users, and can be efficiently induced from data.<ref name="ref_94181150">[https://link.springer.com/10.1007%2F978-0-387-30164-8_204 Decision Tree]</ref>
# An extensive survey of decision tree learning can be found in Murthy (1998).<ref name="ref_94181150" />
# Researchers from various disciplines such as statistics, machine learning, pattern recognition, and Data Mining have dealt with the issue of growing a decision tree from available data.<ref name="ref_42c68945">[https://link.springer.com/chapter/10.1007/0-387-25465-X_9 Decision Trees]</ref>
# This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner.<ref name="ref_42c68945" />
# Now that you know exactly what a decision tree is, it’s time to consider why this methodology is so effective.<ref name="ref_1ecc6d62">[https://venngage.com/blog/what-is-a-decision-tree/ What is a Decision Tree and How to Make One [Templates + Examples]]</ref>
# A decision tree to help someone determine whether they should rent or buy, for example, would be a welcomed piece of content on your blog.<ref name="ref_1ecc6d62" />
# The overarching objective or decision you’re trying to make should be identified at the very top of your decision tree.<ref name="ref_1ecc6d62" />
# When creating your decision tree, it’s important to do research, so you can accurately predict the likelihood for success.<ref name="ref_1ecc6d62" />
# Fig 1. illustrates a learned decision tree.<ref name="ref_775c88ec">[https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-decision-tree/tutorial/ Decision Tree Tutorials & Notes]</ref>
# In Fig 3., we can see that there are two candidate concepts for producing the decision tree that performs the AND operation.<ref name="ref_775c88ec" />
# Attributes is a list of other attributes that may be tested by the learned decision tree.<ref name="ref_775c88ec" />
# #Importing the Decision tree classifier from the sklearn library.<ref name="ref_775c88ec" />
# We developed the additive tree, a theoretical approach to generate a more accurate and interpretable decision tree, which reveals connections between CART and gradient boosting.<ref name="ref_4d83b225">[https://www.pnas.org/content/116/40/19887 Building more accurate decision trees with the additive tree]</ref>
# Decision tree learning and gradient boosting have been connected primarily through CART models used as the weak learners in boosting.<ref name="ref_4d83b225" />
# 26 proves that decision tree algorithms, specifically CART and C4.5 (27), are, in fact, boosting algorithms.<ref name="ref_4d83b225" />
# A sequence of weak classifiers on each branch of the decision tree was trained recursively using AdaBoost; therefore, rendering a decision tree where each branch conforms to a strong classifier.<ref name="ref_4d83b225" />
# Time to shine for the decision tree!<ref name="ref_1368242a">[https://christophm.github.io/interpretable-ml-book/tree.html Interpretable Machine Learning]</ref>
# Individual predictions of a decision tree can be explained by decomposing the decision path into one component per feature.<ref name="ref_1368242a" />
# We want to predict the number of rented bikes on a certain day with a decision tree.<ref name="ref_1368242a" />
# A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.<ref name="ref_b8441c85">[https://en.wikipedia.org/wiki/Decision_tree Decision tree]</ref>
# Drawn from left to right, a decision tree has only burst nodes (splitting paths) but no sink nodes (converging paths).<ref name="ref_b8441c85" />
# The decision tree illustrates that when sequentially distributing lifeguards, placing a first lifeguard on beach #1 would be optimal if there is only the budget for 1 lifeguard.<ref name="ref_b8441c85" />
# A decision tree typically starts with a single node, which branches into possible outcomes.<ref name="ref_0fa0bd9b">[https://www.lucidchart.com/pages/decision-tree What is a Decision Tree Diagram]</ref>
# The construction of decision tree classifier does not require any domain knowledge or parameter setting, and therefore is appropriate for exploratory knowledge discovery.<ref name="ref_7de990f9">[https://www.geeksforgeeks.org/decision-tree/ Decision Tree]</ref>
# Decision tree can be computationally expensive to train.<ref name="ref_7de990f9" />
# The process of growing a decision tree is computationally expensive.<ref name="ref_7de990f9" />
# A decision tree (also referred to as a classification tree or a reduction tree) is a predictive model which is a mapping from observations about an item to conclusions about its target value.<ref name="ref_b4f91818">[https://www.sciencedirect.com/topics/computer-science/decision-trees Decision Trees - an overview]</ref>
# Building a decision tree that is consistent with a given data set is easy.<ref name="ref_b4f91818" />
# Section 17.4.2.1 describes how iComment uses decision tree learning to build models to classify comments.<ref name="ref_b4f91818" />
# iComment uses decision tree learning because it works well and its results are easy to interpret.<ref name="ref_b4f91818" />
# In this article I shall present one recently developed concept called the “decision tree,” which has tremendous potential as a decision-making tool.<ref name="ref_73d96d45">[https://hbr.org/1964/07/decision-trees-for-decision-making Decision Trees for Decision Making]</ref>
# The decision tree can clarify for management, as can no other analytical tool that I know of, the choices, risks, objectives, monetary gains, and information needs involved in an investment problem.<ref name="ref_73d96d45" />
# I illustrates a decision tree for the cocktail party problem.<ref name="ref_73d96d45" />
# In the decision tree you lay out only those decisions and events or results that are important to you and have consequences you wish to compare.<ref name="ref_73d96d45" />
# In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.<ref name="ref_10a640db">[https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052 Decision Trees in Machine Learning]</ref>
# A decision tree is drawn upside down with its root at the top.<ref name="ref_10a640db" />
# This methodology is more commonly known as learning decision tree from data and above tree is called Classification tree as the target is to classify passenger as survived or died.<ref name="ref_10a640db" />
# Now the decision tree will start splitting by considering each feature in training data.<ref name="ref_10a640db" />
# Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning.<ref name="ref_62f2f37f">[https://en.wikipedia.org/wiki/Decision_tree_learning Decision tree learning]</ref>
# It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).<ref name="ref_62f2f37f" />
# In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making).<ref name="ref_62f2f37f" />
# To construct a decision tree on this data, we need to compare the information gain of each of four trees, each split on one of the four features.<ref name="ref_62f2f37f" />
# You start a Decision Tree with a decision that you need to make.<ref name="ref_5703ec85">[https://www.mindtools.com/dectree.html Decision Skills from MindTools.com]</ref>
# Now you are ready to evaluate the decision tree.<ref name="ref_5703ec85" />
# Start on the right hand side of the decision tree, and work back towards the left.<ref name="ref_5703ec85" />
# The use of multi-output trees for regression is demonstrated in Multi-output Decision Tree Regression.<ref name="ref_1403dcd3">[http://scikit-learn.org/stable/modules/tree.html 1.10. Decision Trees — scikit-learn 0.23.2 documentation]</ref>
# C4.5, C5.0 and CART¶ What are all the various decision tree algorithms and how do they differ from each other?<ref name="ref_1403dcd3" />
===소스===
<references />

@@ 121번째 줄: / 121번째 줄: @@
   <references />
-== 메타데이터 ==
+==메타데이터==
 ===위키데이터===
 * ID :  [https://www.wikidata.org/wiki/Q831366 Q831366]

← 이전 판		2020년 12월 26일 (토) 12:17 판
120번째 줄:		120번째 줄:
	===소스===		===소스===
	<references />		<references />
		+
		+	== 메타데이터 ==
		+
		+	===위키데이터===
		+	* ID : [https://www.wikidata.org/wiki/Q831366 Q831366]

결정 트리 - 편집 역사

2021년 2월 17일 (수) 08:07에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단