Data Science Flashcards

What does the term 'hyperparameter' refer to in machine learning?

Answer: Configuration settings set before training that control the learning process

Hyperparameters like learning rate, tree depth, and number of layers are set by the practitioner, not learned from data.

Which of the following best describes transfer learning?

Answer: Reusing a pretrained model's learned representations for a new but related task

Transfer learning fine-tunes a model pretrained on a large dataset (e.g., ImageNet) for a specific downstream task.

What is the role of the kernel in a Support Vector Machine (SVM)?

Answer: It maps data into a higher-dimensional space to find a linear separating hyperplane

Kernel functions compute dot products in a transformed feature space without explicitly computing the transformation.

In Bayesian inference, what does the prior distribution represent?

Answer: Beliefs about the parameters before observing any data

The prior encodes existing knowledge or assumptions about a parameter before data is taken into account.

What is the main difference between bagging and boosting ensemble methods?

Answer: Bagging trains models in parallel on bootstrap samples; boosting trains sequentially, focusing on errors

Bagging reduces variance by averaging parallel models, while boosting reduces bias by iteratively correcting mistakes.

Which Python library is most commonly used for constructing and training deep learning models?

Answer: PyTorch

PyTorch (and TensorFlow/Keras) provide dynamic computation graphs and GPU acceleration essential for deep learning.

What does 'precision' measure in the context of a classification model?

Answer: The fraction of predicted positives that are truly positive

Precision = TP / (TP + FP), measuring how trustworthy a positive prediction is.