Contextual outliers (otherwise known as conditional outliers)are values that significantly deviate from the rest of the data points in the same context, meaning that the same value may not be considered an outlier if it occurred in a different context. Outliers in this category are commonly found...
If an outlier is due to a measurement error, what should we do? A. Keep it in the data. B. Remove it and recalculate. C. Ignore the entire data set. D. Change the measurement method. 相关知识点: 试题来源: 解析 B。解析:如果异常值是由于测量误差导致的,应该将其移除并重新计算。
An outlier, in mathematics, statistics and information technology, is a specific data point that falls outside the range of probability for a data set. In other words, the outlier is distinct from other surrounding data points in a particular way. Outlier analysis is extremely useful in various...
Conversely, since every set of data must have some value that is the largest, as well as one that is the smallest, simply having either of those properties is not enough to flag a datum as an "outlier." We will continue our discussion of outliers in a future column of "Chemometrics in...
Discover the significance of outlier detection in data science and its impact on data quality and analysis. Explore common causes of outliers and the methods used to detect and address them.
insights for business decision-making. The data often is enriched and optimized to make it more informative and useful -- for example, by blending internal and external data sets, creating new data fields, eliminating outlier values and addressing imbalanced data sets that could skew analytics ...
Explanation: This is an outlier identification problem. To solve it, study all 5 options and see what they have in common. In this case, each choice has three shapes. What are those shapes? Each one contains a circle, a triangle, and a square. Except Choice B. Instead of a triangle,...
Data cleansing, data cleaning and data scrubbing are often used interchangeably. For the most part, they're considered to be the same thing. In some cases, though, data scrubbing is viewed as an element of data cleansing that specifically involves removing duplicate, bad, unneeded or old data...
Grid-based clustering algorithms divide the data space into a finite number of cells or grid boxes and assign data points to these cells. The resulting grid structure forms the basis for identifying clusters. An example of a grid-based algorithm is STING (Statistical Information Grid). Grid-base...
Adjusted means are most often used in finance when there are outlier data points that have an outsized impact on the trend line for a data set. An analyst may choose to remove outliers entirely, but this is typically only done in cases where the reasons behind the outliers are known, or...