In L2 regularization (Ridge), the penalty is proportional to the square of the magnitude of the coefficients. This regularization technique adds a penalty term to the loss function based on the sum of squared coefficients, which helps prevent overfitting by discouraging large coefficients.

Data formats like XML allow for tagging or marking up data, which gives it a structured organization. XML is an example of semi-structured data, where data elements are labeled, providing some level of organization.

Random Forest is an ensemble method that builds multiple decision trees. These decision trees are used to make predictions and are combined to create a more accurate and robust model. Random Forest is known for its ability to reduce overfitting and provide reliable predictions, making it a popular choice in machine learning.

Convolutional Neural Networks (CNNs) are commonly used in large-scale image recognition tasks. CNNs have multiple layers that can automatically learn hierarchical features from images, making them well-suited for this purpose.

Canny Edge Detection is a widely used technique for edge detection in images. It applies multiple filters to detect edges with varying intensities, providing information about the location and strength of edges in the image.

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.

The Box Plot, also known as a box-and-whisker plot, considers the spread of data points around the median and helps identify outliers based on the interquartile range (IQR). Outliers are data points that fall outside the whiskers of the box plot. Z-Score is used for standardization, One-Hot Encoding is used for categorical variables, and K-Means Clustering is a clustering technique and not used for identifying outliers.

In Big Data processing, the first blank should be filled with "Filter," and the second blank with "Join." Filtering and sorting are common operations in data preparation, while aggregations and transformations are typically done using join operations.

One-Hot Encoding is a technique used to convert categorical variables into a binary format that machine learning algorithms can understand. It helps prevent a categorical variable's values from being treated as ordinal and is essential for improving the performance of models that use categorical data.