AI Solver

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• AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test

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.

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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)

• ...detect patterns or relationships ... Correlation analysis or Time series analysis
  • ...predict values/quantity/outcomes
  • ...predict categories so I can classify each data point into a specific groups
  • ...cluster data points to discover relationships and structure; find hidden structure
  • ...make a Recommendation
• ...identify the most important features (attributes) or perform Dimensional Reduction
• ...find outliers; unusual points, anomaly detection
• ...find a Generative AI-type solution to identify the most plausible theory among competing explanations
  • 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
• ... automate processes; understand (semantic parsing) complete sentences, understanding synonyms of matching words, Sentiment Analysis, or Speech Recognition, (speech) translation ...Natural Language Processing (NLP)
• ... 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)
• ... train Agents/Assistants or Personal Companions

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.

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• Algorithms & Neural Network Models to learn about approaches used to solve specific AI-related problems
• Model Search
• How to pick an algorithm | Willem Meints
• Model Mindmap | Mindmeister
• An easy guide to choose the right Machine Learning algorithm | Yogita Kinha - KDnuggets
• Everything you should know about AI models | Eray Eliaçık - Dataconomy
• | McKinsey & Company ... A comprehensive guide on selecting AI use cases and matching them to the right model architecture for enterprise scaling.

Microsoft Azure Studio Cheatsheet

• How to choose algorithms for Microsoft Azure Machine Learning | Microsoft
• Models | Hugging Face ... click on Sort: Trending

SAS

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Notes