Define overfitting and underfitting.

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In machine learning, overfitting and underfitting describe two common problems that occur when a model fails to generalize well to unseen data.

Overfitting

• Happens when a model learns the training data too well, including noise and random fluctuations.

• The model becomes overly complex, capturing details that don’t generalize to new data.

• Symptoms:

  • Very high accuracy on training data.

  • Poor performance on test/validation data.

• Example: A decision tree that memorizes every training sample instead of learning general rules.

• Fixes:

  • Simplify the model.

  • Use regularization (L1/L2, dropout).

  • Gather more training data.

  • Apply cross-validation and early stopping.

Underfitting

• Happens when a model is too simple to capture patterns in the data.

• The model fails to learn enough from training data.

• Symptoms:

  • Poor performance on both training and test data.

• Example: Using a linear regression model to predict outcomes from highly nonlinear data.

• Fixes:

  • Use a more complex model.

  • Provide better feature engineering.

  • Train for longer with optimized hyperparameters.

Key Difference

• Overfitting = Model memorizes, not generalizes.

• Underfitting = Model is too weak to learn patterns.

Summary

• Goal of ML is to strike the right balance between underfitting and overfitting.

• A good model captures underlying trends while ignoring noise, ensuring strong performance on unseen data.

Read more :

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Explain supervised vs unsupervised learning.

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