Polynomial features allow the model to capture non-linear relationships between the features and the target variable.

Gini impurity is used to determine the best split at each node in a Decision Tree, helping to create more homogeneous child nodes.

Hierarchical clustering does not require the number of clusters to be predetermined, allowing for more flexibility in analysis.

Hierarchical clustering creates a dendrogram, which provides a visual representation of the data's clustering structure.

CNNs automatically learn features from data, which eliminates the need for manual feature extraction that is often required in traditional methods.

CNNs are specifically designed to process data with a grid-like topology, making them highly effective for image processing tasks.

Cross-validation provides a more reliable estimate of model performance by using different subsets of the data for training and validation.

DBSCAN can find clusters of arbitrary shape, making it more flexible than K-Means.

Ensemble methods leverage the strengths of multiple models to enhance overall predictive performance.

Ensemble methods combine predictions from multiple models, which often leads to improved accuracy and robustness.

Ensemble methods like Random Forest reduce variance by averaging multiple decision trees, leading to improved prediction accuracy.

Ensemble methods combine multiple models to reduce variance and improve overall prediction accuracy.

F1 Score provides a balance between precision and recall, making it more informative than accuracy in certain contexts.

The main advantage of using Gaussian Mixture Models (GMM) is that it can model clusters with different shapes and sizes.

GMM can model clusters with different shapes and sizes, unlike K-Means which assumes spherical clusters.

Hierarchical clustering does not require the number of clusters to be specified in advance, allowing for more flexibility in cluster formation.

A key advantage of hierarchical clustering is that it does not require the number of clusters to be specified in advance, allowing for more flexibility in analysis.

The main advantage of K-means clustering is its simplicity and computational efficiency, making it easy to implement for large datasets.

Neural networks can model complex relationships in data due to their multiple layers and non-linear activation functions.

Pre-trained embeddings save computational resources and time as they leverage knowledge from large datasets.

SVM can effectively handle non-linear relationships through the use of kernel functions, making it versatile for various datasets.

SVM is particularly robust to overfitting in high-dimensional spaces due to its margin maximization approach.

The F1 Score balances precision and recall, making it a better metric than accuracy in cases where class distribution is uneven.

Linear regression assumes that there is a linear relationship between the independent and dependent variables.

A model registry helps manage model versions and associated metadata, facilitating better tracking and governance of deployed models.

The curse of dimensionality makes it difficult for K-means to find meaningful clusters as the distance between points becomes less informative in high dimensions.

Both the Silhouette score and the Elbow method are commonly used criteria for determining the optimal number of clusters in K-means clustering.

Decision trees commonly use criteria like Entropy or Gini Impurity to determine the best feature for splitting nodes.

Agglomerative clustering begins with individual data points and merges them into clusters, while divisive clustering starts with one cluster and splits it into smaller clusters.

Hard clustering assigns each data point to a single cluster, while soft clustering assigns probabilities to each cluster.