BFS uses a queue to keep track of the vertices that need to be explored next, ensuring that vertices are processed in the order they are discovered.

A priority queue is used in Dijkstra's algorithm to efficiently retrieve the next node with the smallest tentative distance.

A queue is used to implement breadth-first search (BFS) as it processes nodes in a first-in, first-out (FIFO) manner.

A queue is used for printer queues because it processes print jobs in the order they are received (FIFO).

'Bagging' refers to the technique of combining multiple models to improve overall performance and reduce variance.

An epoch is one complete pass through the entire training dataset during the training process of a neural network.

Model drift occurs when the statistical properties of the target variable change over time, leading to decreased model performance.

Model drift refers to the phenomenon where a model's performance decreases over time due to changes in the underlying data distribution.

Overfitting occurs when a model learns the training data too well, including noise, resulting in poor generalization to new data.

Training a neural network involves adjusting its weights based on labeled data to minimize the loss function.

A confusion matrix provides a summary of prediction errors by showing the counts of true positives, true negatives, false positives, and false negatives.

A confusion matrix provides a summary of prediction results, showing true positives, false positives, true negatives, and false negatives.

A high AUC value indicates that the model has good discrimination ability between classes.

A high AUC (Area Under the Curve) value indicates that the model has good discrimination ability between the positive and negative classes.

High precision with low recall indicates that the model is good at identifying positive cases but misses many actual positives.

High precision indicates a high number of true positives compared to false positives, meaning the model is good at identifying positive instances.

A high precision value indicates that most of the predicted positives are indeed true positives, reflecting the model's accuracy in positive predictions.

A high ROC AUC score indicates that the model is effective at distinguishing between the positive and negative classes.

A high value of AUC-ROC indicates good model discrimination ability between positive and negative classes.

A high MCC value indicates a strong correlation between predicted and actual classes, reflecting better model performance.

High precision indicates that a large proportion of positive identifications were actually correct.

A high R-squared value indicates that a significant proportion of the variance in the dependent variable is explained by the independent variables in the model.

A high R-squared value indicates that a large proportion of the variance in the dependent variable is predictable from the independent variables.

A/B testing compares the performance of two different models to determine which one performs better in a production environment.

A/B testing helps to evaluate model performance against a baseline by comparing two versions.

A/B testing involves comparing two versions of a model to evaluate which one performs better in a real-world scenario.

Accuracy is defined as the ratio of correctly predicted instances to the total instances, indicating the overall effectiveness of the model.

AUC stands for Area Under the Curve, which quantifies the overall ability of the model to discriminate between positive and negative classes.

BFS stands for Breadth First Search, which is an algorithm for traversing or searching tree or graph data structures.

BFS stands for Breadth First Search, which is an algorithm for traversing or searching tree or graph data structures.