Machine Learning & Data Science Flashcards

Which ensemble method trains multiple models in parallel on random subsets of the training data and aggregates their predictions?

Answer: Bagging

Bagging (Bootstrap Aggregating) trains independent learners on bootstrapped samples and combines them via voting or averaging to reduce variance.

What does the ROC curve plot?

Answer: True Positive Rate vs. False Positive Rate at different thresholds

The ROC curve shows the tradeoff between sensitivity (TPR) and specificity (1-FPR) across all classification thresholds.

Which regularization technique randomly sets a fraction of neuron activations to zero during each training step?

Answer: Dropout

Dropout randomly deactivates neurons during training, forcing the network to learn redundant representations and reducing co-adaptation.

In a confusion matrix for binary classification, which formula correctly computes Precision?

Answer: TP / (TP + FP)

Precision measures what fraction of predicted positives are actually positive: TP divided by all predicted positives (TP + FP).

What is the key difference between supervised and unsupervised learning?

Answer: Supervised learning requires labeled training data; unsupervised discovers patterns without labels

Supervised learning learns a mapping from inputs to known labels, while unsupervised learning finds hidden structure in unlabeled data.

Which metric measures the average magnitude of errors in the same units as the target variable, without squaring them?

Answer: Mean Absolute Error (MAE)

MAE computes the mean of absolute differences between predictions and actuals, keeping error in the original unit scale without amplifying outliers.

What is 'feature engineering' in the context of machine learning?

Answer: Transforming raw data into informative input representations for a model

Feature engineering involves creating, transforming, or selecting variables from raw data to improve model performance.