Regularisation Defender

Defend vulnerable models, tune penalties, prune complexity, stop overfitting, compare validation evidence, and build reliable predictions through strategic interactive machine learning missions under pressure.

Campaign Intermediate Mission 1: Polynomial Panic
Reduce the generalisation gap without making the model underfit.
Mission progress0%
Score: 0 Streak: 0
Train score
0.96
Unregularised
Validation score
0.68
Needs defence
Generalisation gap
0.28
Lower is safer
Complexity
91
0–100 scale
Stability
43
Across folds
Time
02:30
Remaining

Model and regularisation controls

0.25
0.50
0.20
12
2
8
80
0.010
18
0.00
0%

Decision boundary and model complexity

Class A Class B Boundary
Polynomial regression Overfitting detected

Training and validation loss

Complexity versus validation score

Regularisation path

Cross-validation stability

Challenges and mastery

0 / 10

Defence results

MissionTechniqueGapComplexityScoreResult
Complete a defence to build your history.
Best score
0
Mastery
0%
Defences
0

Accessibility and presentation

Keyboard: T train, P pause, R reset, H hint, N next.

Build a safer model

  1. Inspect the initial training and validation gap.
  2. Choose a technique suitable for the current model.
  3. Tune penalties, architecture limits, or stopping rules.
  4. Train and compare curves, stability, and complexity.
  5. Submit when the model generalises without severe underfitting.
Regularisation Defender

Related Calculators

Train the Tiny ModelHyperparameter Tuning RaceEpoch ControlBatch Size ExperimentModel Selection TournamentBias-Variance Balancing GameEarly Stopping Challenge

Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.