Bootstrap sampling is a statistical technique used to estimate the distribution of a dataset by repeatedly sampling from it with replacement. Each sample, known as a bootstrap sample, is of the same size as the original dataset, but because it is sampled with replacement, some data points may appear multiple times while others may not appear at all. This method is commonly used to assess the variability of a statistic, estimate confidence intervals, and improve the robustness of machine learning models.
A bootstrapped dataset refers to a dataset generated by repeatedly sampling from an original dataset with replacement. This means that some data points from the original dataset may appear multiple times in the bootstrapped dataset, while others may not appear at all. Bootstrapping is a statistical method commonly used to estimate the sampling distribution of a statistic by generating multiple bootstrapped datasets, each of which serves as a new sample for analysis.
Bootstrapping meaning refers to a statistical method used to estimate the distribution of a sample statistic by resampling with replacement from the original data. This approach allows for the approximation of the sampling distribution of almost any statistic, such as the mean, median, or variance, by generating multiple simulated samples (known as "bootstrap samples") from the original dataset. Bootstrapping is particularly valuable when the underlying distribution of the data is unknown or when traditional parametric methods are not applicable.
A bounding box is a rectangular or square-shaped box used to define the position and spatial extent of an object within an image or video frame. It is widely used in computer vision tasks such as object detection, image segmentation, and tracking, where the objective is to identify and localize specific objects within visual data.
A bounding polygon is a geometric shape used to precisely define the boundaries of an object within an image or a video frame. Unlike a bounding box, which is rectangular and may include an irrelevant background, a bounding polygon closely follows the contours of the object, providing a more accurate and detailed representation of its shape. This method is commonly used in computer vision tasks such as object detection, image segmentation, and annotation, where precise localization and shape description of objects are important.
A box plot, also known as a box-and-whisker plot, is a graphical representation of the distribution of a dataset. It displays the dataset’s minimum, first quartile (Q1), median, third quartile (Q3), and maximum values, effectively summarizing the central tendency, variability, and skewness of the data. The box plot is a useful tool for identifying outliers, comparing distributions, and understanding the spread of the data.
Brute force search is a straightforward algorithmic approach that systematically checks all possible solutions to a problem until the correct one is found. It involves exploring every possible combination or option in a solution space, making it a simple but often inefficient method, especially when the search space is large. Brute force search is typically used when no better algorithm is available or when the problem size is small enough that all possibilities can be feasibly evaluated.
Business intelligence (BI) refers to the technologies, processes, and practices used to collect, integrate, analyze, and present business data. The goal of BI is to support better decision-making within an organization by providing actionable insights from data. BI systems and tools enable organizations to transform raw data into meaningful information that can be used to drive strategic and operational decisions.