(Boosted) Decision Tree - Revision history

BPeat at 02:52, 6 March 2024

2024-03-06T02:52:49Z

BPeat at 03:53, 2 March 2024

2024-03-02T03:53:32Z

BPeat at 00:55, 14 July 2023

2023-07-14T00:55:23Z

BPeat at 01:47, 6 July 2023

2023-07-06T01:47:34Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T00:57:42Z

Text replacement - "http:" to "https:"

BPeat at 03:00, 24 September 2020

2020-09-24T03:00:35Z

BPeat at 14:57, 23 February 2020

2020-02-23T14:57:37Z

BPeat at 04:08, 3 February 2019

2019-02-03T04:08:47Z

BPeat at 19:45, 12 January 2019

2019-01-12T19:45:13Z

BPeat: BPeat moved page Boosted Decision Tree to (Boosted) Decision Tree without leaving a redirect

2019-01-06T05:52:45Z

BPeat moved page Boosted Decision Tree to (Boosted) Decision Tree without leaving a redirect

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=boosted+decision+tree+machine+learning+ML+artificial+intelligence ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]

← Older revision		Revision as of 03:53, 2 March 2024
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	* [https://www.gearpatrol.com/tech/a364310/what-is-machine-learning/ What Is Machine Learning and Why Does It Matter? \| Jeremy Fischer - Gear Patrol]		* [https://www.gearpatrol.com/tech/a364310/what-is-machine-learning/ What Is Machine Learning and Why Does It Matter? \| Jeremy Fischer - Gear Patrol]

−	A boosted decision tree is an ensemble learning method in which the second tree corrects for the errors of the first tree, the third tree corrects for the errors of the first and second trees, and so forth. Predictions are based on the entire ensemble of trees together that makes the prediction. For further technical details, see the Research section of this article. Generally, when properly configured, boosted decision trees are the easiest methods with which to get top performance on a wide variety of machine learning tasks. However, they are also one of the more memory-intensive learners, and the current implementation holds everything in memory. Therefore, a boosted decision tree model might not be able to process the very large datasets that some linear learners can handle.	+	A boosted decision tree is an ensemble learning method in which the second tree corrects for the errors of the first tree, the third tree corrects for the errors of the first and second trees, and so forth. Predictions are based on the entire ensemble of trees together that makes the prediction. For further technical details, see the Research section of this article. Generally, when properly configured, boosted decision trees are the easiest methods with which to get top performance on a wide variety of machine learning tasks. However, they are also one of the more [[memory]]-intensive learners, and the current implementation holds everything in [[memory]]. Therefore, a boosted decision tree model might not be able to process the very large datasets that some linear learners can handle.

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=boosted+decision+tree+machine+learning+ML+artificial+intelligence ...Google search]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=boosted+decision+tree+artificial+intelligence YouTube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...predict categories]]
 * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 * [[Gradient Boosting Machine (GBM)]]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=boosted+decision+tree+artificial+intelligence YouTube search...]
+[https://www.youtube.com/results?search_query=boosted+decision+tree+artificial+intelligence YouTube search...]
-[http://www.google.com/search?q=boosted+decision+tree+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=boosted+decision+tree+machine+learning+ML+artificial+intelligence ...Google search]
 * [[AI Solver]]
@@ Line 13: / Line 13: @@
 * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 * [[Gradient Boosting Machine (GBM)]]
-* [http://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
+* [https://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
-* [http://xgboost.readthedocs.io/en/latest/model.html Introduction to Boosted Trees | XGBoost]
+* [https://xgboost.readthedocs.io/en/latest/model.html Introduction to Boosted Trees | XGBoost]
-* [http://www.unite.ai/what-is-a-decision-tree/ What is a Decision Tree? | Daniel Nelson - Unite.ai]
+* [https://www.unite.ai/what-is-a-decision-tree/ What is a Decision Tree? | Daniel Nelson - Unite.ai]
-* [http://www.gearpatrol.com/tech/a364310/what-is-machine-learning/ What Is Machine Learning and Why Does It Matter? | Jeremy Fischer - Gear Patrol]
+* [https://www.gearpatrol.com/tech/a364310/what-is-machine-learning/ What Is Machine Learning and Why Does It Matter? | Jeremy Fischer - Gear Patrol]
 A boosted decision tree is an ensemble learning method in which the second tree corrects for the errors of the first tree, the third tree corrects for the errors of the first and second trees, and so forth. Predictions are based on the entire ensemble of trees together that makes the prediction. For further technical details, see the Research section of this article. Generally, when properly configured, boosted decision trees are the easiest methods with which to get top performance on a wide variety of machine learning tasks. However, they are also one of the more memory-intensive learners, and the current implementation holds everything in memory. Therefore, a boosted decision tree model might not be able to process the very large datasets that some linear learners can handle.
-<img src="http://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/cart.png" width="800">
+<img src="https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/cart.png" width="800">
-<img src="http://hips.hearstapps.com/amv-prod-gp.s3.amazonaws.com/gearpatrol/wp-content/uploads/2017/07/3049155-inline-i-1-machine-learning-is-just-a-big-game-of-plinko.gif" width="800">
+<img src="https://hips.hearstapps.com/amv-prod-gp.s3.amazonaws.com/gearpatrol/wp-content/uploads/2017/07/3049155-inline-i-1-machine-learning-is-just-a-big-game-of-plinko.gif" width="800">
@@ Line 33: / Line 33: @@
 == Two-Class Boosted Decision Tree ==
-[http://www.youtube.com/results?search_query=Two-Class+boosted+decision+tree+artificial+intelligence YouTube search...]
+[https://www.youtube.com/results?search_query=Two-Class+boosted+decision+tree+artificial+intelligence YouTube search...]
-* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-boosted-decision-tree Two-Class Boosted Decision Tree | Microsoft]
+* [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-boosted-decision-tree Two-Class Boosted Decision Tree | Microsoft]
 <youtube>Q1N3ZVe1jvY</youtube>

← Older revision		Revision as of 04:08, 3 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=boosted+decision+tree+artificial+intelligence YouTube search...]		[http://www.youtube.com/results?search_query=boosted+decision+tree+artificial+intelligence YouTube search...]
		+	[http://www.google.com/search?q=boosted+decision+tree+machine+learning+ML+artificial+intelligence ...Google search]

	* [[AI Solver]]		* [[AI Solver]]

← Older revision	Revision as of 05:52, 6 January 2019
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