The True Positive Rate, also known as Sensitivity or Recall, calculates the ratio of true positives to the sum of true positives and false negatives. It measures the model's ability to correctly identify positive cases. It is an important metric in binary classification evaluation.

k-NN imputation is a technique that uses the similarity of data points to impute missing values. It finds records with similar characteristics to the one with missing data and replaces the missing value with the imputed value from its nearest neighbors. Other options are simpler imputation methods.

Long Short-Term Memory (LSTM) is a variant of RNN designed to handle long-term dependencies by maintaining a cell state that can capture information over long sequences. LSTM's ability to store and retrieve information over extended time steps makes it well-suited for tasks involving long-term dependencies in data sequences.

The F1 Score is a metric that balances the trade-off between precision and recall. It is especially useful when dealing with imbalanced datasets or when you want to find a balance between correctly identifying positive cases (precision) and capturing all positive cases (recall). The F1 Score is the harmonic mean of precision and recall. It is a suitable choice for evaluating models when both precision and recall are essential.

Transfer learning allows the startup to use pre-trained deep neural networks (e.g., a pre-trained CNN) as a starting point. This approach significantly reduces training time and computational resources, while still benefiting from the capabilities of deep learning.

In real-time data processing, Apache Spark is commonly used to ingest and process streaming data. Spark provides the capabilities to handle streaming data in real time, making it a popular choice for such applications.

The median is the most resistant measure of central tendency in a skewed distribution. It is less affected by extreme values or outliers since it represents the middle value when the data is arranged in order. The mean, mode, and geometric mean can be heavily influenced by outliers, causing them to be less representative of the data's central location.

Regularization is a common technique used to prevent overfitting in linear regression models. It adds a penalty term to the linear regression's cost function to discourage overly complex models. Regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.

Transfer learning in computer vision involves fine-tuning a pre-trained model to adapt it for a new task. It leverages knowledge from a source task to improve performance on a target task, making it more efficient and effective than training from scratch.

Macro-averaging computes the average metric score for each class, treating all other classes as the "negative" class. It provides an equal weight to each class and is useful when you want to assess the model's overall performance while giving equal importance to each class, regardless of class size. Macro-averaging can be particularly useful in imbalanced multi-class classification problems.