Data Mining UOG — University of Gujrat 2023

UNIVERSITY OF GUJRAT

Hafiz Hayat Campus (Morning)

FINAL TERM EXAMINATIONS

FALL-2023

Course Code : IT-446

Course Title : Data Mining

Section A

Q2 Answer the following Questions.

i) What are over fitted models? Explain their effects on performance.

Ans : Definition: Overfitting occurs when a machine learning model learns the training data too well,

capturing noise or random fluctuations in the data instead of just the underlying patterns.

Causes:

• Too complex model architecture with too many parameters.

• Too many features relative to the amount of training data.

• Lack of regularization techniques to prevent overfitting.

Effects on Performance:

• Poor Generalization: Overfitted models may perform exceptionally well on the training data but

fail to generalize to new, unseen data.

• High Variance: Overfitting increases the variance of the model, making it sensitive to small

fluctuations in the training data.

• Loss of Interpretability: Capturing noise as meaningful patterns, leading to less interpretable

and useful models.

• Increased Error on Test Data: The model tends to perform poorly on new, unseen data because

it has essentially memorized the training set rather than learning the underlying patterns.

• Complex Models: Overfitting often results from overly complex models that capture noise

rather than the true underlying relationships in the data.

• Loss of Predictive Power: Overfitted models may not provide accurate predictions or

classifications for real-world scenarios due to their focus on training data specifics.