克－吉二氏法英文解释翻译、克－吉二氏法的近义词、反义词、例句
英语翻译：
【医】 Kramer-Gittleman's method
分词翻译：
克的英语翻译：
gram; gramme; overcome; restrain
【医】 G.; Gm.; gram; gramme
吉的英语翻译：
auspicious; dexter; lucky; propitious
【计】 giga
二的英语翻译：
twin; two
【计】 binary-coded decimal; binary-coded decimal character code
binary-to-decimal conversion; binary-to-hexadecimal conversion
【医】 bi-; bis-; di-; duo-
氏的英语翻译：
family name; surname
法的英语翻译：
dharma; divisor; follow; law; standard
【医】 method
【经】 law
网络扩展解释
克－吉二氏法
克－吉二氏法（Kechiji method）是一种用于数据分类的方法。它可以根据属性值对数据进行处理和分类，是数据挖掘领域中常用的技术。其中文拼音为kè-jí-èr shì fǎ。
英文解释翻译
The Kechiji method is a technique used for data classification. It enables the processing and classification of data based on attribute values. It is a commonly used method in the field of data mining.
英文读音
The English pronunciation of Kechiji is keh-chi-jee.
英文用法
The Kechiji method is a technique used in data mining for classifying data according to attribute values. It can be applied to a wide variety of data sets, making it a versatile tool for data analysis. The method can help researchers and analysts make sense of complex data sets, identify patterns, and make predictions based on data trends.
英文例句
Here are some examples of how the Kechiji method might be used in practice:
- Market research: Using the Kechiji method to analyze customer data in order to identify patterns and preferences.
- Healthcare: Analyzing data from medical records to identify patterns in patient outcomes.
- Fraud detection: Using the Kechiji method to identify unusual patterns in financial transactions.
英文近义词
Other methods that are commonly used for data classification include:
- Decision trees
- K-nearest neighbors
- Neural networks
- Support vector machines
英文反义词
There are no formal antonyms to the Kechiji method, as it is a specific technique for data classification. However, other methods that are not used for data classification might be considered the opposite of the Kechiji method.
英文单词常用度
The Kechiji method is a technical term that is not commonly used outside of the field of data mining. As such, its usage frequency in everyday English is relatively low.