Ai Overfitting Underfitting — Free AI & ML Tutorial

TL;DR: Learn Ai Overfitting Underfitting in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.

Ai Overfitting Underfitting in AI & ML

Overfitting happens when a model learns the training data too well — including the noise and random fluctuations — so it performs great on training data but poorly on new data. Like a student who memorises exam answers without understanding.

Underfitting happens when a model is too simple to capture the underlying pattern. It performs poorly on both training and test data. Like using a straight line to fit data that curves.

Signs of overfitting: Training accuracy is much higher than test/validation accuracy. The model memorises specific examples rather than learning general rules.

Solutions to overfitting: More training data, regularisation (L1/L2), dropout (neural networks), cross-validation, simpler model (fewer parameters), early stopping, data augmentation. Solutions to underfitting: More complex model, more features, more training epochs.

# Diagnose with learning curves:
# - Plot training accuracy vs validation accuracy over epochs
# - Overfitting:   train acc rises, val acc falls (gap widens)
# - Underfitting:  both low and flat
# - Good fit:      both rise and converge
#
# Regularisation in sklearn:
# Ridge (L2): LinearRegression → Ridge(alpha=1.0)
# Lasso (L1): Lasso(alpha=0.1)

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