K-Means is effective for identifying spherical clusters due to its centroid-based approach.

DBSCAN is effective for identifying clusters of varying shapes and densities, making it suitable for non-spherical clusters.

K-means is a popular clustering algorithm used to group similar documents based on their content.

K-Means is a popular clustering algorithm used for customer segmentation due to its efficiency and simplicity.

DBSCAN is effective for identifying clusters of varying shapes and densities, as it does not assume spherical clusters.

DBSCAN is effective for large datasets and can identify clusters of varying shapes while handling noise.

DBSCAN can automatically determine the number of clusters based on the density of data points.

DBSCAN is effective for large datasets with noise as it can identify clusters of varying shapes and sizes while ignoring outliers.

K-means clustering is more sensitive to outliers because it uses mean values to determine cluster centroids, which can be skewed by extreme values.

Hierarchical clustering is more suitable for discovering nested clusters, as it creates a tree structure that can reveal relationships at various levels of granularity.

DBSCAN is particularly effective for discovering clusters of varying shapes and sizes, making it suitable for non-globular data distributions.

DBSCAN is more suitable for discovering non-linear relationships in data as it can identify clusters of varying shapes and sizes.

Hierarchical clustering can be more suitable for discovering non-spherical clusters as it does not assume a specific shape for the clusters.

K-means clustering is particularly effective for large datasets due to its efficiency and scalability.

Hierarchical clustering is suitable for discovering natural groupings in data by creating a tree of clusters.

DBSCAN can automatically determine the number of clusters based on the density of data points.

DBSCAN is effective for large datasets with noise as it can identify clusters of varying shapes and sizes while ignoring outliers.

Hierarchical clustering is suitable for discovering natural groupings in data by creating a tree-like structure of clusters.

Canary deployment allows for a gradual rollout of a new model version to a small subset of users before full deployment.

All of the mentioned strategies (blue-green, canary, and rolling deployments) allow for gradual rollout of new models to minimize risk.

Blue-Green Deployment allows for quick rollback by maintaining two identical environments, enabling seamless switching between them if issues arise.

Canary deployment is a strategy where a new model is gradually rolled out to a small subset of users to monitor its performance before full deployment.

Canary deployment involves gradually rolling out a new model to a small subset of users to monitor its performance before a full rollout.

Canary deployment gradually rolls out a new model to a small subset of users to monitor its performance before a full rollout.

K-means typically uses Euclidean distance to measure the distance between data points and centroids.

The F1 Score is suitable for customer churn prediction as it balances precision and recall, which is important in identifying customers who are likely to churn.

All of the mentioned metrics (Accuracy, F1 Score, and Log Loss) can be used to evaluate multi-class classification problems, each providing different insights into model performance.

Silhouette Score is used to evaluate clustering algorithms by measuring how similar an object is to its own cluster compared to other clusters.

Mean Absolute Error (MAE) is commonly used to assess the performance of regression models as it measures the average absolute errors.

F1 Score is better for imbalanced classification as it considers both precision and recall.