Both overfitting and underfitting can occur depending on the number of features selected.

Choosing the right number of clusters (K) is a common challenge in K-Means clustering.

SVM can be computationally intensive and may struggle with scalability when dealing with large datasets due to the complexity of the optimization problem.

Intermediate code is generally easier to analyze than high-level code due to its simpler structure and reduced abstraction.

Accuracy is a common evaluation metric for classification models, measuring the proportion of correct predictions made by the model.

Accuracy is a common metric used to evaluate the performance of classification models like Decision Trees and Random Forests.

Mean Squared Error is commonly used to evaluate the performance of RNNs in regression tasks.

Accuracy is a common metric used to evaluate the performance of SVM classifiers.

Accuracy is a common evaluation metric for classification models, measuring the proportion of correct predictions.

A common initialization method for K-means is to randomly select data points from the dataset to serve as the initial centroids.

LL parsers cannot handle ambiguous grammars, which limits their applicability.

Permutation importance is a common method used to evaluate feature importance in Random Forests by measuring the increase in prediction error when the feature's values are permuted.

All of the above methods can be used to handle missing data, depending on the context and the amount of missingness.

A/B testing is commonly used to compare the performance of the deployed model against a baseline or alternative model in real-time.

A/B testing is commonly used to monitor deployed models by comparing the performance of two different versions in real-world scenarios.

The Elbow method is commonly used to determine the optimal number of clusters by plotting the explained variance against the number of clusters.

All of the listed techniques can be applied during the optimization phase of intermediate code generation to improve performance.

All these factors can lead to poor model performance and generalization.

Regularly retraining the model with new data helps ensure its reliability and accuracy in changing environments.

Identifying fraudulent transactions often involves feature engineering to create relevant features that can help distinguish between legitimate and fraudulent activities.

Feature engineering is applicable in various real-world scenarios, including image classification, spam detection, and customer segmentation.

Binary search is commonly used in applications with sorted data because it efficiently narrows down the search space.

Rolling updates allow for gradual deployment of new model versions while minimizing disruption to the service.

Balanced trees like AVL and Red-Black trees are commonly used to maintain a sorted list of items, allowing for efficient search and update operations.

Predictive maintenance is a common use case, where cloud ML services analyze data to predict equipment failures.

Real-time fraud detection is a common use case for cloud ML services, leveraging machine learning to identify fraudulent activities.

Random Forests are widely used in credit scoring due to their ability to handle large datasets and provide robust predictions.

Red-Black trees are commonly used to implement associative arrays (or maps) due to their efficient insertion, deletion, and lookup operations.

Red-Black trees are commonly used to implement associative arrays (or maps) due to their efficient search, insert, and delete operations.

Red-Black trees are often used to implement associative arrays due to their balanced nature.