When two classes have a similar number of instances nearby, using an odd 'k' value can help avoid ambiguity in classifying the test data point, as it prevents ties. An even 'k' can lead to ties, making classification less clear.

Explainability is the term that refers to the ability to understand and interpret machine learning model decisions. It's crucial for trust and accountability in AI systems.

Long Short-Term Memory (LSTM) is designed to capture long-term dependencies in sequences by maintaining cell states that are less susceptible to the vanishing gradient problem.

Generative Adversarial Networks (GAN) are designed to generate synthetic data that is highly realistic. In the context of generating profile pictures, GANs can produce images that are often indistinguishable from real photos, making them an ideal choice for this task.

Deep Q Networks are advantageous due to their capability to handle high-dimensional observation spaces. This is crucial when dealing with complex real-world data, as in image-based environments.

Transparency in a model is the ability to explain its decision-making, which is crucial for trust, auditing, and regulatory compliance.

Natural Language Processing (NLP) is the most suitable approach for extracting insights from textual data like doctor's notes and patient feedback, which can help in reducing readmission rates.

The primary challenge of the multi-armed bandit problem is to balance Exploration (trying new actions) and Exploitation (choosing known good actions) to maximize cumulative rewards in a limited time.

In logistic regression, the log odds of the dependent variable is modeled using the Logit function. The Logit function is the inverse of the sigmoid function and is used to map linear combinations of independent variables to the range of real numbers.

When a model consistently predicts the same output, it is considered to have high "consistency." This means it's not providing useful or varied predictions, which can be a problem in machine learning.