# 機器學習詞彙的重要字詞Machine learning glossary of important terms

## 資料Data

• 由資料行和資料列組成are made up of columns and rows
• 延遲評估，即作業呼叫它時，它們只載入資料are lazily evaluated, that is they only load data when an operation calls for it
• 包含定義每個資料行類型、格式和長度的結構描述contain a schema that defines the type, format and length of each column

## 評估工具Estimator

ML.NET 中實作 IEstimator<TTransformer> 介面的類別。A class in ML.NET that implements the IEstimator<TTransformer> interface.

## Loss 函式Loss function

Loss 函式是定型標籤值和模型所做預測之間的差異。A loss function is the difference between the training label values and the prediction made by the model. 模型的參數是透過將 loss 函式降到最低來評估。The parameters of the model are estimated by minimizing the loss function.

## 正規化Regularization

• $L_1$ 正規化零加權不顯著的特性。$L_1$ regularization zeros weights for insignificant features. 在這種正規化後，已儲存的模型大小可能會變得較小。The size of the saved model may become smaller after this type of regularization.
• $L_2$ 正規化會最小化不顯著特性的加權範圍，這是更一般的程序，且對極端值較不敏感。$L_2$ regularization minimizes weight range for insignificant features, This is a more general process and is less sensitive to outliers.