Random Forests can handle missing values by using surrogate splits, which allow the model to make predictions even when some data points are missing.

Random Forest improves accuracy by averaging the predictions of multiple Decision Trees, which reduces variance.

After deletion in a Red-Black tree, balance is restored through a combination of rotations and recoloring.

Red-Black Trees maintain balance after insertion by performing rotations and recoloring nodes to satisfy the properties of the tree.

An AVL tree maintains balance after an insertion by performing rotations (single or double) to ensure the height difference property is satisfied.

AVL trees maintain balance by performing rotations (single or double) after insertions to ensure the balance factor remains within the allowed range.

Binary search calculates the middle element using the formula (low + high) / 2, where low and high are the current bounds.

Binary search calculates the middle index using (low + high) / 2 to find the midpoint of the current search range.

Binary search determines the next interval by comparing the target with the middle element and deciding which half to continue searching.

Binary search compares the middle element of the array with the target value to decide whether to search the left or right half.

Dijkstra's algorithm ensures it finds the shortest path by always choosing the nearest unvisited vertex, which is a key aspect of its greedy approach.

Dijkstra's algorithm uses a greedy approach, always expanding the least costly node first, ensuring that the shortest path is found.

Once a node has been visited and its shortest distance determined, Dijkstra's algorithm ignores it in future iterations.

Dijkstra's algorithm updates the tentative distances of neighboring nodes by adding the edge weights to the current node's distance.

Dijkstra's algorithm updates the tentative distances by adding the edge weights to the current distance of the node.

Random Forest can use surrogate splits to handle missing values, allowing it to make predictions even with incomplete data.

Random Forest can use surrogate splits to handle missing values, allowing it to make predictions even when some data is missing.

Random Forest improves accuracy by averaging the predictions of multiple trees, which reduces overfitting.

Random Forest reduces overfitting by averaging the predictions of multiple trees, which smooths out the noise and variance.

SVM can handle multi-class classification using one-vs-one or one-vs-all strategies.

SVM can handle outliers using a soft margin approach, which allows some misclassifications to achieve a better overall model.

The balancing factor of an AVL tree node is calculated as the height of the left subtree minus the height of the right subtree.

AVL trees are more rigidly balanced than Red-Black trees, which allows AVL trees to provide faster lookups at the cost of more complex insertions and deletions.

The choice of kernel significantly impacts the model's ability to capture the underlying patterns in the data, thus affecting performance.

AVL trees are more strictly balanced, which can lead to a shorter height compared to Red-Black trees in the worst case.

Insertion in Red-Black Trees typically requires fewer rotations compared to AVL Trees.

Insertion in an AVL tree may require more rotations to maintain balance compared to a Red-Black tree, which allows for a more relaxed balancing approach.

Binary search can return any index of the duplicates, as it does not guarantee which duplicate will be found first.

Both Red-Black Trees and AVL Trees have a search time complexity of O(log n).

Both Red-Black Trees and AVL Trees have a search time complexity of O(log n).