Mixed Deck — All Machine Learning Topics Flashcards

What does the Actor-Critic method combine in reinforcement learning?

Answer: A policy network (actor) that selects actions combined with a value network (critic) that evaluates them

Actor-Critic methods combine an actor (policy network) that selects actions with a critic (value network) that evaluates those actions, reducing variance compared to pure policy gradient methods.

Which of the following best describes 'stratified k-fold cross-validation'?

Answer: Each fold preserves the same class distribution as the full dataset

Stratified k-fold ensures each fold has roughly the same proportion of class labels as the original dataset, important for imbalanced data.

What is the primary consideration when implementing changes to neural networks?

Answer: Impact assessment and change management

Impact assessment and proper change management ensure that modifications do not introduce unexpected problems or service disruptions.

Which of the following is an example of a regression problem in supervised learning?

Answer: Predicting the selling price of a house based on its features

Predicting house prices produces a continuous numeric output, making it a regression problem rather than a classification problem.

In a confusion matrix, what does a False Negative represent?

Answer: Model predicted negative but the true label was positive

A False Negative occurs when the model predicts the negative class but the true label is actually positive, meaning a positive case was missed.

What is the fundamental principle behind feature engineering in the Machine Learning domain?

Answer: Balancing performance, reliability, and efficiency

Effective technical design requires balancing performance requirements with reliability needs and operational efficiency.

Which unsupervised technique is most suitable for finding association rules like 'customers who buy X also buy Y'?

Answer: Apriori algorithm (frequent itemset mining)

The Apriori algorithm mines frequent itemsets from transaction data and derives association rules based on support, confidence, and lift metrics.

What is the role of learning objectives in instructional design for Machine Learning?

Answer: They define measurable outcomes and guide instruction

Learning objectives define specific, measurable outcomes that guide both instruction and assessment, ensuring alignment and clarity of purpose.

Which linkage criterion in hierarchical clustering tends to produce compact, spherical clusters?

Answer: Ward linkage

Ward linkage minimizes the total within-cluster variance at each merge step, producing compact and roughly spherical clusters.

In the context of SVMs, what does the 'kernel trick' allow?

Answer: Mapping data into a higher-dimensional space without explicit computation

The kernel trick implicitly maps data to a higher-dimensional feature space using a kernel function, enabling linear separation of nonlinearly separable data.

Which professional attribute is most valued in classification within the Machine Learning field?

Answer: Accountability and commitment to standards

Accountability and commitment to professional standards build trust and ensure consistent, high-quality practice.

When using one-hot encoding for categorical features, what problem can arise with high-cardinality variables?

Answer: Dimensionality explosion with many sparse binary columns

High-cardinality categorical variables create an extremely large and sparse feature matrix when one-hot encoded, increasing memory and computation costs.

What does validity mean when selecting an assessment tool for Machine Learning certification purposes?

Answer: The tool measures what it claims to measure

Validity indicates that an assessment tool accurately measures the construct or ability it was designed to measure.

What is the purpose of cross-validation?

Answer: Both A and B

Explanations: Cross-validation is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set.

In logistic regression, what function maps the linear output to a probability between 0 and 1?

Answer: Sigmoid

The sigmoid function squashes any real-valued number to the range (0, 1), making it suitable for binary probability estimation.

What is 'feature scaling' and why is it important for algorithms like SVMs and k-NN?

Answer: Normalizing feature ranges so no single feature dominates distance calculations

Feature scaling (e.g., standardization or min-max normalization) ensures all features contribute equally to distance-based calculations, which is critical for SVMs and k-NN.

In a decision tree, what does 'pruning' accomplish?

Answer: Removes branches that provide little predictive power to reduce overfitting

Pruning removes overly specific branches from a decision tree to improve generalization on unseen data.

In reinforcement learning, what is an 'episode'?

Answer: A complete sequence from the initial state to a terminal state

An episode is a complete sequence of interactions from the starting state to a terminal or goal state in the environment.

A business wants to develop a fraud detection model. Due to the limited number of fraud incidents, the Data Scientist currently does not have enough information. Which strategy is the MOST LIKELY to catch the MOST genuine fraud cases?

Answer: Oversampling using SMOTE

Fraud detection datasets are typically highly imbalanced, with very few fraud cases. To effectively train a model to identify these rare instances, the dataset needs to be balanced. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class (fraud cases) based on existing minority class examples, rather than simply duplicating them, which helps the model learn the characteristics of fraud cases more effectively and increases the likelihood of catching genuine fraud.

What is the purpose of a rubric in Machine Learning assessment?

Answer: To provide clear criteria and consistent evaluation standards

Rubrics provide clear, transparent criteria that ensure consistent and fair evaluation across different assessors and assessment instances.