Gradient Boosting Machine (GBM) - Revision history

BPeat at 02:50, 6 March 2024

2024-03-06T02:50:51Z

BPeat at 01:09, 14 July 2023

2023-07-14T01:09:07Z

BPeat at 01:45, 6 July 2023

2023-07-06T01:45:01Z

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

2023-03-28T21:06:42Z

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

BPeat at 17:05, 17 August 2020

2020-08-17T17:05:58Z

BPeat at 17:05, 17 August 2020

2020-08-17T17:05:19Z

BPeat at 16:56, 17 August 2020

2020-08-17T16:56:58Z

BPeat at 16:53, 17 August 2020

2020-08-17T16:53:23Z

BPeat at 20:03, 27 July 2020

2020-07-27T20:03:29Z

BPeat at 19:58, 27 July 2020

2020-07-27T19:58:45Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Boosted+Decision+Tree+Regression+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 values]]
 * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Boosted+Decision+Tree+Regression+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 values]]
 * [[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+Regression YouTube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...predict values]]
 * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 * [[(Boosted) Decision Tree]]

@@ 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+Regression YouTube search...]
+[https://www.youtube.com/results?search_query=Boosted+Decision+Tree+Regression YouTube search...]
-[http://www.google.com/search?q=Boosted+Decision+Tree+Regression+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Boosted+Decision+Tree+Regression+machine+learning+ML+artificial+intelligence ...Google search]
 * [[AI Solver]]
@@ Line 14: / Line 14: @@
 * [[(Boosted) Decision Tree]]
 * [[Boosted Random Forest]]
-* [http://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
+* [https://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
 * [[XGBoost; eXtreme Gradient Boosted trees]]
 * [[LightGBM]]   ...Microsoft's gradient boosting framework that uses tree based learning algorithms
-* [http://towardsdatascience.com/boosting-algorithm-gbm-97737c63daa3 Boosting algorithm: GBM | SauceCat - Towards Data Science]
+* [https://towardsdatascience.com/boosting-algorithm-gbm-97737c63daa3 Boosting algorithm: GBM | SauceCat - Towards Data Science]
 It is also known as Multiple Additive Regression Trees (MART), Boosted Decision Tree Regression, and Gradient Boosted Regression Trees (GBRT).
-the ensemble is a collection of models that do not predict the real objective field of the ensemble, but rather the improvements needed for the function that computes this objective. ...the modeling process starts by assigning some initial values to this function, and creates a model to predict which gradient will improve the function results. The next iteration considers both the initial values and these corrections as its original state, and looks for the next gradient to improve the prediction function results even further. The process stops when the prediction function results match the real values or the number of iterations reaches a limit. As a consequence, all the models in the ensemble will always have a numeric objective field, the gradient for this function. The real objective field of the problem will then be computed by adding up the contributions of each model weighted by some coefficients. If the problem is a classification, each category (or class) in the objective field has its own subset of models in the ensemble whose goal is adjusting the function to predict this category. [http://blog.bigml.com/2017/03/14/introduction-to-boosted-trees/ Introduction to Boosted Trees | bigML]
+the ensemble is a collection of models that do not predict the real objective field of the ensemble, but rather the improvements needed for the function that computes this objective. ...the modeling process starts by assigning some initial values to this function, and creates a model to predict which gradient will improve the function results. The next iteration considers both the initial values and these corrections as its original state, and looks for the next gradient to improve the prediction function results even further. The process stops when the prediction function results match the real values or the number of iterations reaches a limit. As a consequence, all the models in the ensemble will always have a numeric objective field, the gradient for this function. The real objective field of the problem will then be computed by adding up the contributions of each model weighted by some coefficients. If the problem is a classification, each category (or class) in the objective field has its own subset of models in the ensemble whose goal is adjusting the function to predict this category. [https://blog.bigml.com/2017/03/14/introduction-to-boosted-trees/ Introduction to Boosted Trees | bigML]
 https://littleml.files.wordpress.com/2017/03/boosted-trees-process.png?w=497

@@ Line 15: / Line 15: @@
 * [[Boosted Random Forest]]
 * [http://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
 * [[LightGBM]]   ...Microsoft's gradient boosting framework that uses tree based learning algorithms
 * [http://towardsdatascience.com/boosting-algorithm-gbm-97737c63daa3 Boosting algorithm: GBM | SauceCat - Towards Data Science]