Accuracy can be misleading in imbalanced datasets, as it may give a false sense of model performance by not reflecting the true predictive power for minority classes.

The main limitation of accuracy is that it does not consider false positives and false negatives, which can be misleading in imbalanced datasets.

Accuracy can be misleading in cases of class imbalance, as it does not differentiate between types of errors.

An AVL tree is a self-balancing binary search tree where the difference in heights between the left and right subtrees cannot be more than one.

Red-Black trees allow for more unbalanced structures than AVL trees, which maintain a stricter balance.

An AVL tree maintains a balance factor of -1, 0, or 1 for every node, ensuring that the tree remains balanced.

The main purpose of a database management system (DBMS) is to store and retrieve data.

A router's main purpose is to route data between different networks, making decisions based on IP addresses.

A VLAN (Virtual Local Area Network) segments a network into different broadcast domains, enhancing security and reducing congestion.

The main purpose of an algorithm is to solve problems.

The main purpose of an API is to allow different software applications to communicate.

The main purpose of balancing in these trees is to ensure that operations can be performed in O(log n) time.

Feature importance helps identify which features contribute most to the predictions made by the Random Forest model.

Pruning reduces the size of the tree, which helps to prevent overfitting and improves generalization to unseen data.

Pruning is used to reduce the size of the tree and prevent overfitting, leading to better generalization on unseen data.

The ARP (Address Resolution Protocol) is used to resolve IP addresses to MAC addresses in a local network.

The forget gate in an LSTM determines what information from the previous cell state should be discarded.

The main purpose of the HTTP protocol is to transfer web pages and resources over the internet.

The main purpose of the Network layer is path determination and logical addressing to route packets across networks.

The partitioning step in Quick Sort rearranges elements around a pivot to ensure that elements less than the pivot are on one side and those greater are on the other.

The softmax function normalizes the output of the final layer to a probability distribution over multiple classes in classification tasks.

The main purpose of clustering methods is to group similar data points, helping to uncover patterns and insights in the data.

Cross-validation is used to reduce overfitting by ensuring that the model performs well on unseen data.

Cross-validation helps in assessing how the model will generalize to an independent dataset, thus providing a better estimate of its performance.

Distance metrics are used in clustering algorithms to measure the similarity or dissimilarity between data points, which is crucial for forming clusters.

Embeddings are used to capture the semantic meaning of words in a continuous vector space.

The silhouette coefficient evaluates the compactness and separation of clusters, helping to assess the quality of clustering results.

Binary search can only be applied to a sorted array.

The maximum depth of a binary tree can be 'n' in the case of a skewed tree.

The maximum depth of a Decision Tree can be controlled by hyperparameters, which helps manage overfitting.