Bootstrap sampling allows Random Forests to create multiple subsets of the training data, which helps in building diverse trees.

The 'Cache-Control' header specifies caching policies for responses, helping to control how and for how long responses are cached by clients and intermediaries.

The 'Content-Type' header indicates the media type of the resource being sent in the HTTP response, helping the client understand how to process it.

'free' is used to release memory that was previously allocated with 'malloc' or similar functions.

The 'gamma' parameter in the RBF kernel controls the influence of a single training example, affecting the decision boundary's shape.

'min_samples_split' controls the minimum number of samples required to split an internal node, helping to prevent overfitting.

'n_estimators' specifies the number of trees in the Random Forest, which affects the model's performance and stability.

The 'return' statement is used to return a value from a function to the caller.

The 'User-Agent' header is used to identify the client software making the request, providing information about the browser and operating system.

The 'visited' array in Dijkstra's algorithm is used to track which vertices have been processed to avoid reprocessing them.

AUC measures the model's performance across all classification thresholds, indicating its ability to distinguish between classes.

AUC-ROC evaluates the trade-off between the true positive rate and false positive rate across different thresholds.

The confusion matrix visualizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.

The default gateway routes traffic from a local network to destinations outside that network, typically between subnets.

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

The elbow method is used to determine the optimal number of clusters by plotting the explained variance as a function of the number of clusters and identifying the 'elbow' point.

The F-test is used to determine the overall significance of the regression model.

The forget gate in an LSTM decides what information to discard from the previous cell state.

The Cache-Control header is used to specify directives for caching mechanisms in both requests and responses.

The HTTP POST method is used to send data to the server, often resulting in a change in state or side effects on the server.

The HTTP status code 404 indicates that the requested resource was not found on the server.

The intercept represents the predicted value of the dependent variable when all independent variables are equal to zero.

The loss function quantifies how well the neural network's predictions match the actual target values.

The Network Layer (Layer 3) is responsible for routing packets across different networks and managing logical addressing.

The network portion of an IP address identifies the specific network to which the device belongs.

The Physical Layer (Layer 1) is responsible for the transmission of raw bitstreams over a physical medium, including electrical signals and light pulses.

The pooling layer reduces the spatial size of the representation, which decreases the number of parameters and computation in the network.

R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables in a regression model.

R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables in the model.

The ROC curve illustrates the performance of a binary classifier as its discrimination threshold is varied, showing the trade-off between true positive rate and false positive rate.