Cross-validation is a technique that helps prevent overfitting by ensuring the model's performance is evaluated on different subsets of the data.

The bottom-up approach is commonly used in dynamic programming to build solutions iteratively from smaller subproblems.

Dropout is a regularization technique that randomly sets a fraction of the neurons to zero during training to prevent overfitting.

K-Means Clustering is primarily a clustering technique, not a feature selection method.

Gradient descent is an optimization algorithm, not a tokenization technique.

K-Means Clustering is an unsupervised learning algorithm used for clustering, not for feature selection.

Principal Component Analysis (PCA) is a widely used technique for reducing the dimensionality of data.

Pruning the tree involves removing sections of the tree that provide little power in predicting target variables, thus preventing overfitting.

Dropout layers randomly deactivate neurons during training, which helps prevent overfitting.

TensorFlow Serving is a popular tool specifically designed for deploying machine learning models in production environments.

TensorFlow Serving is a popular tool specifically designed for deploying machine learning models.

Lex is a tool specifically designed for generating lexical analyzers.

All three traversal methods can be implemented using a stack, either explicitly or through the call stack in recursion.

Any of the traversal methods (pre-order, in-order, post-order) can be used to create a mirror image of a binary tree.

In-order traversal of a binary search tree visits nodes in sorted order, making it the correct choice.

In-order traversal of a binary search tree visits nodes in sorted order, as it processes the left subtree, then the root, and finally the right subtree.

Level-order traversal visits nodes level by level, making it suitable for printing nodes in that order.

Level-order traversal uses a queue to keep track of nodes at the current level before moving to the next level.

Level Order traversal uses a queue to keep track of nodes at each level.

All fundamental operations (insertion, deletion, and searching) in a Red-Black tree are guaranteed to be O(log n) due to the tree's balanced structure.

All of the operations (insertion, deletion, and searching) in an AVL tree are guaranteed to be O(log n) due to its balanced structure.

All of the operations (insertion, deletion, and searching) are guaranteed to be O(log n) in both AVL and Red-Black trees.

All of the operations: insertion, deletion, and search are guaranteed to take O(log n) time in a Red-Black tree.

Deletion in a Red-Black tree is more complex due to the need to maintain the tree's properties, often requiring multiple rotations and color changes.

Deletion in an AVL tree is more complex due to the need for multiple rotations to maintain balance.

Deletion in AVL trees can be more complex due to the need for multiple rotations to maintain balance.

Deletion in AVL trees can be more complex due to the need for multiple rotations to maintain balance.

Insertion operations in Red-Black trees are generally more efficient than in AVL trees due to fewer rotations required.

Searching is more efficient in AVL trees because they are more strictly balanced than Red-Black trees, leading to a shorter height.

Searching is generally more efficient in AVL trees due to their stricter balancing, which keeps the tree height lower than that of Red-Black trees.