The best-case time complexity of Merge Sort is O(n log n), as it always divides the array regardless of the order.

The best-case time complexity of Quick Sort is O(n log n), which occurs when the pivot divides the array into two equal halves.

The broadcast address for the subnet 192.168.1.0/24 is 192.168.1.255, used to send data to all hosts in that subnet.

The broadcast address for a /24 subnet is the last address in the range, which is 192.168.10.255.

The CIDR notation for a subnet mask of 255.255.255.128 is /25, indicating that the first 25 bits are used for the network.

The CIDR notation for a subnet mask of 255.255.255.192 is /26, indicating 26 bits are used for the network part.

The subnet mask 255.255.255.224 corresponds to a CIDR notation of /27, which means 27 bits are used for the network part.

The subnet mask 255.255.255.248 corresponds to /29, as it has 29 bits set to 1.

In a Red-Black tree, each node is colored either red or black, which helps maintain balance during insertions and deletions.

The correct syntax to include a header file in C is #include <header.h>.

The default port number for HTTP is 80.

The default subnet mask for a Class C IP address is 255.255.255.0, allowing for 256 addresses in the subnet.

On-policy learning updates the policy based on actions taken by the current policy, while off-policy learning can learn from actions taken by a different policy.

Adding more features can lead to overfitting and reduce the interpretability of the model, especially if the features are not relevant.

Adding more predictors can lead to overfitting, especially if the additional predictors do not contribute meaningful information.

Increasing the number of trees can improve model accuracy due to better averaging, but there are diminishing returns after a certain point.

Increasing the regularization parameter (C) decreases the margin width, allowing the model to fit the training data more closely.

Multicollinearity can make coefficient estimates unstable and difficult to interpret in a linear regression model.

Multicollinearity makes coefficient estimates unstable, leading to difficulties in interpreting the model.

Outliers can significantly skew the results of a linear regression model, affecting both the slope and intercept.

Outliers can significantly distort the cluster centroids in K-means clustering, leading to inaccurate clustering results.

Using a linear kernel in SVM means that the model can only create linear decision boundaries, making it suitable for linearly separable data.

A soft margin allows for some misclassifications, which can help in achieving better generalization on unseen data.

A very small value for 'C' allows for a wider margin, potentially leading to more misclassifications but better generalization.

Binary search is not applicable to linked lists due to their non-contiguous memory allocation.

Polynomial features allow the model to capture non-linear relationships between the features and the target variable.

The first step is to initialize the distance to the source node as 0 and all other nodes as infinity.

The first usable IP address in the subnet 10.0.0.0/22 is 10.0.0.1, as 10.0.0.0 is the network address.

The first usable IP address in the subnet 10.0.0.0/24 is 10.0.0.1, as 10.0.0.0 is the network address.

In the subnet 192.168.1.0/24, the first valid host address is 192.168.1.1, as 192.168.1.0 is the network address.