Thinking·OS

Graph explorer

Explore the knowledge graph

Analogy depends on Pattern. Activate to inspect this relation.Cross-validation measures Overfitting. Activate to inspect this relation.Dimensionality reduction constrains Overfitting. Activate to inspect this relation.Dimensionality reduction is a Unsupervised learning. Activate to inspect this relation.Feedback influences Machine learning. Activate to inspect this relation.Generalization is analogous to Analogy. Activate to inspect this relation.Generalization enables Prediction. Activate to inspect this relation.Gradient descent applies to Optimization. Activate to inspect this relation.Gradient descent depends on Training data. Activate to inspect this relation.Machine learning is part of Artificial intelligence. Activate to inspect this relation.Machine learning depends on Optimization. Activate to inspect this relation.Machine learning enables Generalization. Activate to inspect this relation.Machine learning depends on Pattern. Activate to inspect this relation.Machine learning depends on Probability. Activate to inspect this relation.Machine learning is a Algorithm. Activate to inspect this relation.Model bias is derived from Training data. Activate to inspect this relation.Overfitting depends on Training data. Activate to inspect this relation.Overfitting is a Trade-off. Activate to inspect this relation.Overfitting is part of Bias-variance tradeoff. Activate to inspect this relation.Overfitting suppresses Generalization. Activate to inspect this relation.Regularization constrains Overfitting. Activate to inspect this relation.Reinforcement learning is a Machine learning. Activate to inspect this relation.Reinforcement learning is part of Machine learning. Activate to inspect this relation.Supervised learning depends on Training data. Activate to inspect this relation.Supervised learning enables Generalization. Activate to inspect this relation.Supervised learning enables Prediction. Activate to inspect this relation.Supervised learning is part of Machine learning. Activate to inspect this relation.Training data enables Machine learning. Activate to inspect this relation.Unsupervised learning is a Machine learning. Activate to inspect this relation.Unsupervised learning is part of Machine learning. Activate to inspect this relation.OverfittingGeneralizationTraining dataTrade-offBias-variance tradeoffCross-validationDimensionality reductionRegularizationPredictionMachine learningAnalogySupervised learningModel biasGradient descentUnsupervised learningPatternAlgorithmOptimizationFeedbackProbabilityReinforcement learningArtificial intelligence
Relationship types

12 concepts viewed through this lens. Bridge concepts connect this view to Algorithms, Artificial Intelligence, Biochemistry, Bioinformatics….

Legend
  • Focused concept
  • Connected concept
  • Bridge concept (just outside the lens)
  • Arrow points from cause / source to effect / target
  • A line with no arrow is a two-way relationship
  • Node colour marks the concept’s primary discipline
22 concepts30 relationships33 disciplines6 relation families

Overfitting

Open concept →

At a glance

When a model clings so tightly to its training examples that it captures noise instead of the real pattern, and then fails on new cases.

Disciplines
Machine Learning · Statistics · Data Science
Role in the graph
Cross-disciplinary bridge — reaches Biology, Business, Cognitive Science, Computer Science, Design, Economics, Engineering, Ethics, Political Science, Public Policy, Systems Engineering
Relationships
7 · 4 relation families
Mental models
1

What am I looking at?

In this lens (12)

Bridge concepts (40)

Just outside the lens — they connect it to other context.

  • Analogy2
    Cognitive Science, Education, Educational Science, Linguistics, Literature, Philosophy · connects to Generalization, Pattern
  • Artificial intelligence2
    Artificial Intelligence, Computer Science · connects to Artificial neural network, Machine learning
  • Dimensionality reduction2
    Data Science · connects to Overfitting, Unsupervised learning
  • Loss function2
    Data Science · connects to Gradient descent, Supervised learning
  • Optimization2
    Business, Economics, Engineering, Mathematical Modelling, Optimization, Systems Engineering · connects to Gradient descent, Machine learning

Insights from this view

Structural observations about the concepts shown here — descriptions of this graph, not claims about the world.

  • This view connects 33 disciplines: Algorithms, Artificial Intelligence, Biology, Business, Cognitive Science, Computer Science, Data Science, Design, Discrete Mathematics, Economics, Education, Educational Science, Engineering, Ethics, Human-Computer Interaction, Linguistics, Literature, Logic, Machine Learning, Mathematical Modelling, Mathematics, Optimization, Philosophy, Philosophy of Science, Political Science, Probability, Public Policy, Software Engineering, Statistics, Systems Biology, Systems Engineering, Systems Science, Theory of Computation.
  • Overfitting is a bridge concept — viewed here through Data Science, Machine Learning, Statistics.
  • The connections here span 6 relation families.
  • Trade offs explains 3 concepts in this view (Biology, Business, Data Science, Design, Economics, Engineering, Ethics, Machine Learning, Mathematical Modelling, Optimization, Political Science, Public Policy, Statistics, Systems Engineering).

Relationships as a list

The focused concept’s relationships. Pick another concept in the graph above to update this list.

Explore through a different lens

A lens is a deterministic projection of the graph. Pick a discipline, thinking pattern or journey to reframe the whole view.

By discipline

ChemistryBiologyPhysicsMathematicsComputer ScienceGeologyMineralogyEconomics

By thinking pattern

Bayesian updatingCause and effectCompoundingConservationConstraintsDefense in depthDiffusion & spreadEmergenceEnergy transformationEntropy & irreversibilityEquilibriumFeedback loopIncentivesInformationLevels & hierarchyModularityNetworksOptimizationOscillationProbabilityRecursion & self-similarityScaleSignal vs noiseSource–pathway–receptorStocks and flowsSymmetry & invarianceThresholds & tipping pointsTrade-offsTriangulationVariation & selectionWaves

By journey

From a Leaf to the ClimateWhy Music Moves UsHow Machines LearnReasoning about a Leaf and the Climate

Concept collections

Concept collections are curated lenses onto the fabric — themed sets of ideas that recur across disciplines. They are not journeys; they are a way to read the graph.

About this view

What this is

Start from one concept and expand outward. The view never shows everything at once — click a node to refocus, filter by relationship type, or switch to an accessible list.

One fabric

3750 concepts and 5051 typed relations form one connected component — no isolated silo.

How to read it

Focus a concept, or apply a lens (discipline, mental model, journey) to see only the threads that matter.