Decision Forest Regression - Revision history

BPeat at 02:50, 6 March 2024

2024-03-06T02:50:22Z

BPeat at 03:52, 2 March 2024

2024-03-02T03:52:38Z

BPeat at 01:10, 14 July 2023

2023-07-14T01:10:13Z

BPeat at 01:44, 6 July 2023

2023-07-06T01:44:30Z

BPeat at 15:31, 4 June 2023

2023-06-04T15:31:02Z

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

2023-03-28T13:02:56Z

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

BPeat at 14:48, 12 October 2020

2020-10-12T14:48:48Z

BPeat at 18:00, 26 April 2020

2020-04-26T18:00:41Z

BPeat at 06:20, 13 July 2019

2019-07-13T06:20:29Z

BPeat at 15:58, 12 July 2019

2019-07-12T15:58:15Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Decision+Forest+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]]
 * [[Regression]] Analysis

@@ Line 28: / Line 28: @@
 Decision trees are non-parametric models that perform a sequence of simple tests for each instance, traversing a binary tree data structure until a leaf node (decision) is reached. Decision trees have these advantages:
-* They are efficient in both computation and memory usage during training and prediction.
+* They are efficient in both computation and [[memory]] usage during training and prediction.
 * They can represent non-linear decision boundaries.
 * They perform integrated feature selection and classification and are resilient in the presence of noisy features.

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Decision+Forest+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]]
 * [[Regression]] Analysis
-* [[Math for Intelligence]] ... [[Finding Paul Revere]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/decision-forest-regression Decision Forest Regression | Microsoft]
 * [https://datascience.stackexchange.com/questions/9159/when-to-choose-linear-regression-or-decision-tree-or-random-forest-regression When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange]

@@ 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=Decision+Forest+Regression YouTube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...predict values]]
 * [[Regression]] Analysis
 * [[Math for Intelligence]] ... [[Finding Paul Revere]]

@@ Line 12: / Line 12: @@
 * [[Capabilities]]
 * [[Regression]] Analysis
-* [[Math for Intelligence]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]]
 * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/decision-forest-regression Decision Forest Regression | Microsoft]
 * [https://datascience.stackexchange.com/questions/9159/when-to-choose-linear-regression-or-decision-tree-or-random-forest-regression When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange]

@@ 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=Decision+Forest+Regression YouTube search...]
+[https://www.youtube.com/results?search_query=Decision+Forest+Regression YouTube search...]
-[http://www.google.com/search?q=Decision+Forest+Regression+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Decision+Forest+Regression+machine+learning+ML+artificial+intelligence ...Google search]
 * [[AI Solver]]
@@ Line 13: / Line 13: @@
 * [[Regression]] Analysis
 * [[Math for Intelligence]]
-* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/decision-forest-regression Decision Forest Regression | Microsoft]
+* [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/decision-forest-regression Decision Forest Regression | Microsoft]
-* [http://datascience.stackexchange.com/questions/9159/when-to-choose-linear-regression-or-decision-tree-or-random-forest-regression When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange]
+* [https://datascience.stackexchange.com/questions/9159/when-to-choose-linear-regression-or-decision-tree-or-random-forest-regression When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange]
-* [http://towardsdatascience.com/data-science-concepts-explained-to-a-five-year-old-ad440c7b3cbd Data Science Concepts Explained to a Five-year-old | Megan Dibble - Toward Data Science]
+* [https://towardsdatascience.com/data-science-concepts-explained-to-a-five-year-old-ad440c7b3cbd Data Science Concepts Explained to a Five-year-old | Megan Dibble - Toward Data Science]
 * [[Feature Exploration/Learning]]