DMC Troubleshooting & Problem Resolution 4

Question 1

A model's performance varies widely depending on how data is split into train and test sets. What technique gives a more reliable estimate?

Accepted Answer

k-fold cross-validation

Answer

Cross-validation averages performance over multiple splits and reduces split-related variance.

Question 2

Both training and test errors are high for a model. What does this indicate?

Accepted Answer

Underfitting or high bias

Answer

Poor performance on both sets means the model is too simple or features are inadequate.

Question 3

Association rule mining returns millions of rules, most of them trivial. What is the best way to manage this?

Accepted Answer

Raise minimum support and confidence and filter by lift

Answer

Higher thresholds and lift filtering keep only strong, meaningful rules.

Question 4

A neural network's loss becomes NaN during training. Which is the most likely cause to check?

Accepted Answer

A learning rate that is too high or unscaled inputs

Answer

Exploding gradients from a high learning rate or unscaled data commonly produce NaN loss.

Question 5

A text-mining model performs poorly because the vocabulary is dominated by words like 'the' and 'and'. What preprocessing step helps?

Accepted Answer

Stop-word removal and TF-IDF weighting

Answer

Removing common words and weighting by TF-IDF emphasizes informative terms.

DMC - Data Mining Certification Practice Test