generalization的音標(biāo)是[?d?en?r?la??ze??n]，基本翻譯是“一般化，概括化”，速記技巧可以是將其拆分為單詞部分進(jìn)行記憶。

Generalization這個詞的詞源可以追溯到拉丁語generalis，意為“普遍的”或“一般的”。Generalization通過借詞形式演變?yōu)橛⑽膯卧~，其變化形式包括名詞形式generalization和形容詞形式general。

相關(guān)單詞：

1. General：作為形容詞和名詞，意為“普遍的”或“一般的”，與generalization有密切關(guān)系，因為generalization就是對一般性的歸納或概括。

2. Generalist：一個名詞，指具有廣泛知識或興趣的人，通常指在多個領(lǐng)域都有一定了解的人。這個詞也反映了generalization的概念，因為generalist是對多個領(lǐng)域的一般性了解。

3. Generalization Error：在機器學(xué)習(xí)中，泛化錯誤是指模型未能適應(yīng)新的、未見過的數(shù)據(jù)或情境。這個詞反映了generalization的概念，即模型應(yīng)該能夠從已知的一般性規(guī)律中推斷出新的具體情況。

4. Generalization Error Rate：在統(tǒng)計和機器學(xué)習(xí)中，泛化誤差率是指模型在未見過的數(shù)據(jù)上的錯誤率。這個詞也反映了generalization的概念，因為泛化誤差率是模型在未知情況下的表現(xiàn)。

5. Generalization Problem：在機器學(xué)習(xí)中，泛化問題是指模型在未見過的數(shù)據(jù)上表現(xiàn)不佳的問題。這個問題通常是由于模型過于簡單或過于特殊化導(dǎo)致的，需要使用更復(fù)雜的模型或更全面的數(shù)據(jù)來解決。

以上這些單詞都反映了generalization的概念和重要性，即從已知的一般性規(guī)律中推斷出新的具體情況的能力。這種能力對于機器學(xué)習(xí)、人工智能和數(shù)據(jù)分析等領(lǐng)域至關(guān)重要。

常用短語：

1. generalize from experience

2. generalize to other cases

3. generalize a concept

4. generalize a theory

5. generalize a hypothesis

6. generalize a rule

7. generalize a principle

雙語例句：

1. Based on my experience, I can generalize that this method works well for most cases.

2. We can"t simply generalize from one example to other cases.

3. The theory we developed can be generalized to other similar situations.

4. The hypothesis we tested doesn"t generalize well to other contexts.

5. We need to be careful not to generalize too much when making judgments about people.

6. The rule we observed in one situation may not generalize to other situations.

7. The principle we discovered has broad applications in many fields.

英文小作文：

Generalization is an essential part of scientific research and development, as it allows us to make connections between different ideas and apply them to new situations. Generalizing from previous experience or experiments can help us identify patterns and trends that can be used to develop new theories, hypotheses, and rules. However, generalization is not always straightforward, and it requires careful consideration and testing to ensure that the generalization is valid and applicable in different contexts.

For example, when developing a new product or service, we need to consider how it will be used in different markets and industries. Generalizing from one market or industry may not be applicable in other contexts, as different factors such as consumer preferences, regulations, and competition may vary significantly. Similarly, when developing a new technology or process, we need to consider how it will perform under different conditions and how it will be used by different users. Generalizing from one case or experiment may not be sufficient to fully understand the potential and limitations of the technology or process. Therefore, generalization is an essential part of scientific research and development, but it requires careful consideration and testing to ensure that the generalization is valid and applicable in different contexts.