In a binary tree of height h, the maximum number of leaf nodes is 2^(h-1), which occurs in a complete binary tree.

The maximum number of nodes at depth d in a binary tree is 2^d.

The maximum number of nodes at level 'h' in a binary tree is 2^h.

The maximum number of nodes at level 'l' in a binary tree is 2^l.

In a binary tree, the maximum number of nodes at level k is 2^k, as each node can have two children.

The minimum number of nodes required to have a height of h in a binary tree is h, which occurs in a skewed tree.

The time complexity of finding the height of a binary tree is O(n) because we may need to visit all nodes.

In the worst case, searching for an element in a binary tree can take O(n) time, especially if the tree is unbalanced.

In-order traversal visits each node exactly once, resulting in a time complexity of O(n).

In-order traversal is a type of DFS as it explores the left subtree, then the node, and finally the right subtree.

Linear regression can be used to forecast sales based on advertising spend, predicting continuous outcomes.

In a city map scenario, Dijkstra's algorithm is most relevant for finding the fastest route between two locations.

Recurrent Neural Networks (RNNs) are commonly used in natural language processing due to their ability to handle sequential data.

The number of bedrooms is a relevant feature that can significantly impact house prices in a predictive model.

The Elbow method helps identify the optimal number of clusters by plotting the explained variance against the number of clusters.

A high R-squared with high MSE suggests that while the model explains a lot of variance, it may be overfitting the training data and not generalizing well.

A high R-squared value with poor performance on new data suggests that the model may be overfitting, capturing noise rather than the underlying trend.

High precision and low recall indicate that the model is good at identifying positive cases but fails to capture many actual positives.

The F1 score is calculated as 2 * (precision * recall) / (precision + recall), which results in 0.72.

Classifying emails as spam or not spam is an example of supervised learning, where the model is trained on labeled data.

Cross-validation helps assess how well the feature engineering has improved model performance.

All methods can be valid depending on the context and the amount of missing data.

Return on Investment (ROI) is often used to evaluate the success of a deployed model, as it measures the financial benefits gained from the model compared to its costs.

In a circular linked list, the last node points back to the first node, forming a circle.

The enqueue operation in a circular queue can be performed in constant time O(1) as long as there is space available.

In a circular queue, the condition rear == (front + 1) % size indicates that the queue is full, as it means there is no space left for new elements.

In a circular queue, when the rear pointer reaches the end of the array, it resets to the beginning of the array to utilize the available space.

A confusion matrix represents the performance of a classification model by showing the true positive, true negative, false positive, and false negative counts.

Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.

The Silhouette Score is commonly used to evaluate the quality of clusters by measuring how similar an object is to its own cluster compared to other clusters.