The "Testing set" is a set of input variables and corresponding target values used to evaluate a Machine Learning model's performance. It helps in assessing how well the model will perform on unseen data.

F1-Score is the harmonic mean of Precision and Recall. It helps balance both metrics, particularly when there's an uneven class distribution. It's often used when both false positives and false negatives are important to minimize.

Bootstrapping is essential in statistical analysis because it allows estimating the distribution of a statistic, even with a small sample. By repeatedly resampling with replacement, it creates numerous "bootstrap samples," enabling the calculation of standard errors, confidence intervals, and other statistical properties.

A decision boundary is a hypersurface that partitions the underlying feature space into classes. It plays a crucial role in determining the class label of a new data point based on which side of the boundary it lies.

Pruning a Decision Tree leads to a simpler model, which can increase bias but reduce variance. This tradeoff helps to avoid overfitting the training data and often results in a model that generalizes better to unseen data.

The Kernel Trick helps in dealing with non-linear data by mapping it into a higher-dimensional space where it can be linearly separated.

Hyperparameters are the settings or parameters of a machine learning model that are defined prior to training and are fine-tuned to optimize performance.

True Negatives (TN) in a Confusion Matrix represent cases where the model correctly predicted the negative class. It indicates that the negative instances were classified correctly.

Deep Learning models are adept at handling unstructured data and finding complex patterns, making them suitable for such a dataset.

A smaller K value results in a more complex decision boundary, while a larger K value makes it smoother.