D491 Introduction to Analytics - Set 3 - Part 3

Correct Answer: Supervised learning

Supervised learning involves training models with labeled data, where each input has a corresponding target output.

Correct Answer: Ridge regression

Ridge regression is a regularization technique that reduces the impact of multicollinearity by adding a penalty to the regression coefficients.

Correct Answer: Text preprocessing (tokenization, stemming)

Text preprocessing techniques like tokenization and stemming are crucial steps to convert raw text into a format suitable for machine learning models.

Correct Answer: The number of clusters

The elbow method helps determine the optimal number of clusters by plotting the within-cluster variance and identifying the point where adding more clusters yields diminishing returns.

Correct Answer: ROC Curve

The ROC Curve (Receiver Operating Characteristic) evaluates a model's ability to distinguish between positive and negative classes across different thresholds.

Correct Answer: To prevent overfitting by halting training once performance stops improving

Early stopping prevents overfitting in neural networks by stopping the training process once performance on the validation set no longer improves.

Correct Answer: Dijkstra's algorithm

Dijkstra's algorithm is a popular graph-based algorithm used to find the shortest path between nodes in a graph.

Correct Answer: To reduce variance by combining multiple models

Bagging (Bootstrap Aggregating) reduces variance by training multiple models on different subsets of the data and combining their predictions.

Correct Answer: Decision tree

Decision trees are capable of detecting both linear and nonlinear relationships in data by recursively splitting the dataset based on feature values.

Correct Answer: DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) does not require the user to pre-specify the number of clusters and can detect clusters of varying shapes.