Difference between revisions of "AI Solver"

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* [[About Algorithms & Neural Network Models]] to learn about approaches used to solve specific AI-related problems
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__NOTOC__
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{{#seo:
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|title=AI Solver
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|titlemode=append
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|keywords=AI Solver, Machine Learning, Algorithm Selection, Discriminative AI, Generative AI, Agentic Workflows, SLM, LLM, Model Evaluation, Train Validate Test, Data Science, AI Strategy
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|description=A guide to selecting the right AI algorithm for your specific task, featuring decision frameworks for discriminative vs. generative models and modern model selection criteria.
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}}
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[https://www.youtube.com/results?search_query=AI+model+selection+guide+2026 YouTube]
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[https://www.quora.com/search?q=how+to+choose+the+right+AI+model ... Quora]
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[https://www.google.com/search?q=AI+algorithm+selection+decision+tree ...Google search]
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[https://news.google.com/search?q=AI+model+selection+trends+2026 ...Google News]
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[https://www.bing.com/news/search?q=AI+model+selection+trends+2026&qft=interval%3d%228%22 ...Bing News]
  
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^ [[Model Search]]
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* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
  
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The AI Solver aids in selecting a starting algorithm for you solution by navigating the hyperlinks. At that point test similar algorithms to better understand and see which one works be for the task and data at hand.
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<i>Aids in selecting a starting algorithm for your solution; at that point discover similar algorithms to see which works best for your task (and data) at hand.</i>
  
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Lets get going? I want to...
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== Lets get going? ==
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I want to...
  
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* [[...predict values]]
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=== Decision Framework: Discriminative vs. Generative ===
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* [[...predict categories]] so I can classify each data point into a specific groups
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To determine your path, follow this logic:
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* [[...cluster]] data points to discover structure  
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* **Is the goal to classify or predict based on existing patterns?** → Use **Discriminative AI** (e.g., Classification, Regression, Anomaly Detection).
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* **Is the goal to create new content or simulate data distributions?** → Use **Generative AI** (e.g., Text Generation, Image Synthesis, Data Augmentation).
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* **Are you building an autonomous system?** → Evaluate **Agentic Workflow Selection**:
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** If the task requires high-precision, low-latency, or structured tool-calling: Use **Small Language Models (SLMs)**.
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** If the task requires broad reasoning, cross-domain abstraction, or multi-step planning: Use **Large Language Models (LLMs)** or a hybrid architecture.
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=== Recommended Algorithm Families ===
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{| class="wikitable"
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! Task Type !! Recommended Algorithm Families
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|-
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| Clustering || K-Means, DBSCAN, Hierarchical Clustering
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|-
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| Forecasting || ARIMA, Prophet, LSTM, Transformer-based Time Series
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|-
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| Classification || Logistic Regression, SVM, Random Forest, Gradient Boosted Trees
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|-
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| Anomaly Detection || Isolation Forest, One-Class SVM, Autoencoders
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|-
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| Generative Tasks || GANs, Diffusion Models, Autoregressive Transformers (LLMs)
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|-
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| Pathfinding/Control || Q-Learning, Deep Q-Network (DQN), PPO (Reinforcement Learning)
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|}
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* ...detect patterns or relationships ... [[Causation vs. Correlation|Correlation analysis]] or [[Forecasting|Time series analysis]]
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** [[...predict values]]/quantity/outcomes
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** [[...predict categories]] so I can classify each data point into a specific groups
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** [[...cluster]] data points to discover relationships and structure; find hidden structure  
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** ...make a [[Recommendation]]
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* ...identify the most important features (attributes) or perform [[Dimensional Reduction]]
 
* [[...find outliers]]; unusual points, anomaly detection
 
* [[...find outliers]]; unusual points, anomaly detection
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* ...find a [[Generative AI]]-type solution to identify the most plausible theory among competing explanations
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** [https://hbr.org/2023/03/a-framework-for-picking-the-right-generative-ai-project A Framework for Picking the Right Generative AI Project | A Framework for Picking the Right Generative AI Project | M. Zao-Sanders & M. Ramos - Harvard Business Review]
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* ... automate processes; understand (semantic parsing) complete sentences, understanding synonyms of matching words, [[Sentiment Analysis]], or [[Speech Recognition]], (speech) translation ...[[Natural Language Processing (NLP)]]
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* ... [[Reinforcement Learning (RL) |pathfinding]]; learn a series of actions; find the best/shortest route to an objective; win a game, traveling salesman problem ... [[Q Learning]], [[Deep Q Network (DQN)]]
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* ... train [[Agents/Assistants]] or [[Personal Companions]]
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== Model Evaluation Metrics ==
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To verify if your chosen algorithm is performing optimally, you must implement a rigorous evaluation strategy linked to [[Train, Validate, and Test]] workflows:
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* **Holdout Validation:** Split data into training and testing sets to measure generalization.
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* **Cross-Validation:** Use K-Fold techniques to ensure performance stability across different data subsets.
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* **Task-Specific Metrics:**
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** Classification: Accuracy, Precision, Recall, F1-Score, Confusion Matrix.
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** Regression: Mean Squared Error (MSE), R-Squared.
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** Agentic/Generative: Instruction-following rate, Tool-call correctness, Groundedness, and LLM-as-a-judge scores.
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_____________________________________________________________________________________
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* [[Algorithms]] & Neural Network Models to learn about approaches used to solve specific AI-related problems
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* [[Model Search]]
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* [https://www.mindmeister.com/app/map/927441936?fullscreen=1&v=public How to pick an algorithm | Willem Meints] 
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* [https://www.mindmeister.com/app/map/927441936?fullscreen=1&v=public Model Mindmap | Mindmeister]
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* [https://www.kdnuggets.com/2020/05/guide-choose-right-machine-learning-algorithm.html An easy guide to choose the right Machine Learning algorithm | Yogita Kinha - KDnuggets]
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* [https://dataconomy.com/2023/04/04/best-ai-models-types-how-to-choose-what-is/  Everything you should know about AI models | Eray Eliaçık - Dataconomy]
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* [https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024-gen-ai-adoption-surges | McKinsey & Company] ... A comprehensive guide on selecting AI use cases and matching them to the right model architecture for enterprise scaling.
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https://cdn-images-1.medium.com/max/600/1*iPIGiJIcQjzZheEgTzOnhA.png
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== [https://learn.microsoft.com/en-us/azure/machine-learning/studio/algorithm-cheat-sheet Microsoft Azure Studio Cheatsheet] ==
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*[https://learn.microsoft.com/en-us/azure/machine-learning/studio/algorithm-choice How to choose algorithms for Microsoft Azure Machine Learning | Microsoft]
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* [https://huggingface.co/models Models | Hugging Face] ... click on Sort: Trending
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<img src="https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/studio-overview-diagram/ml_studio_overview_v1.1.png" width="1225" height="900">
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https://msdnshared.blob.core.windows.net/media/TNBlogsFS/prod.evol.blogs.technet.com/CommunityServer.Blogs.Components.WeblogFiles/00/00/01/02/52/AlgoDecisionTree-2.png
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== [https://blogs.sas.com/content/subconsciousmusings/2020/12/09/machine-learning-algorithm-use/ SAS] ==
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* [https://www.sas.com/en_us/solutions/ai.html AI]
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https://blogs.sas.com/content/subconsciousmusings/files/2017/04/machine-learning-cheet-sheet.png
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== Notes ==
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https://cdn-images-1.medium.com/max/800/1*PzeV89iMXPxGMShh6bhwHQ.png
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https://cdn-images-1.medium.com/max/800/1*xlLV8XBECmBTv0dBZKFoyg.png
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https://cdn-images-1.medium.com/max/800/1*qhp867ZtHsO2nPeMdDh4Gw.png
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https://cdn-images-1.medium.com/max/600/1*dgd9vqD96NhUoxUZLMnF_A.png

Latest revision as of 13:59, 19 September 2026

YouTube ... Quora ...Google search ...Google News ...Bing News

Aids in selecting a starting algorithm for your solution; at that point discover similar algorithms to see which works best for your task (and data) at hand.

Lets get going?

I want to...

Decision Framework: Discriminative vs. Generative

To determine your path, follow this logic:

  • **Is the goal to classify or predict based on existing patterns?** → Use **Discriminative AI** (e.g., Classification, Regression, Anomaly Detection).
  • **Is the goal to create new content or simulate data distributions?** → Use **Generative AI** (e.g., Text Generation, Image Synthesis, Data Augmentation).
  • **Are you building an autonomous system?** → Evaluate **Agentic Workflow Selection**:
    • If the task requires high-precision, low-latency, or structured tool-calling: Use **Small Language Models (SLMs)**.
    • If the task requires broad reasoning, cross-domain abstraction, or multi-step planning: Use **Large Language Models (LLMs)** or a hybrid architecture.

Recommended Algorithm Families

Task Type Recommended Algorithm Families
Clustering K-Means, DBSCAN, Hierarchical Clustering
Forecasting ARIMA, Prophet, LSTM, Transformer-based Time Series
Classification Logistic Regression, SVM, Random Forest, Gradient Boosted Trees
Anomaly Detection Isolation Forest, One-Class SVM, Autoencoders
Generative Tasks GANs, Diffusion Models, Autoregressive Transformers (LLMs)
Pathfinding/Control Q-Learning, Deep Q-Network (DQN), PPO (Reinforcement Learning)

Model Evaluation Metrics

To verify if your chosen algorithm is performing optimally, you must implement a rigorous evaluation strategy linked to Train, Validate, and Test workflows:

  • **Holdout Validation:** Split data into training and testing sets to measure generalization.
  • **Cross-Validation:** Use K-Fold techniques to ensure performance stability across different data subsets.
  • **Task-Specific Metrics:**
    • Classification: Accuracy, Precision, Recall, F1-Score, Confusion Matrix.
    • Regression: Mean Squared Error (MSE), R-Squared.
    • Agentic/Generative: Instruction-following rate, Tool-call correctness, Groundedness, and LLM-as-a-judge scores.

_____________________________________________________________________________________

1*iPIGiJIcQjzZheEgTzOnhA.png

Microsoft Azure Studio Cheatsheet

AlgoDecisionTree-2.png

SAS

machine-learning-cheet-sheet.png

Notes

1*PzeV89iMXPxGMShh6bhwHQ.png 1*xlLV8XBECmBTv0dBZKFoyg.png 1*qhp867ZtHsO2nPeMdDh4Gw.png 1*dgd9vqD96NhUoxUZLMnF_A.png

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