AWS wellarchitected documentation change
Summary
Reordered and clarified definitions of feature engineering components. Changed 'feature engineering is automated' to 'feature extraction is automated' in deep learning context.
Security assessment
Changes are definitional clarifications and terminology corrections without any security context or references to vulnerabilities/protections.
Diff
diff --git a/wellarchitected/latest/machine-learning-lens/feature-engineering.md b/wellarchitected/latest/machine-learning-lens/feature-engineering.md index 9cc4e06b0..1b08eced1 100644 --- a//wellarchitected/latest/machine-learning-lens/feature-engineering.md +++ b//wellarchitected/latest/machine-learning-lens/feature-engineering.md @@ -13 +13 @@ _Figure 11: Feature engineering main components_ -Feature engineering is a process to select and transform variables when creating a predictive model using machine learning or statistical modeling. Feature engineering typically includes feature creation, feature transformation, feature extraction, and feature selection as listed in Figure 11. With deep learning, feature engineering is automated as part of the algorithm learning. +Feature engineering is a process to select and transform variables when creating a predictive model using machine learning or statistical modeling. Feature engineering typically includes feature creation, feature transformation, feature extraction, and feature selection as listed in Figure 11. With deep learning, feature extraction is automated as part of the algorithm learning. @@ -15 +15 @@ Feature engineering is a process to select and transform variables when creating - * **Feature creation** refers to the creation of new features from existing data to help with better predictions. Examples of feature creation include: one-hot-encoding, binning, splitting, and calculated features. + * **Feature selection** is the process of selecting a subset of extracted features. This is the subset that is relevant and contributes to minimizing the error rate of a trained model. Feature importance score and correlation matrix can be factors in selecting the most relevant features for model training. @@ -19 +19 @@ Feature engineering is a process to select and transform variables when creating - * **Feature extraction** involves reducing the amount of data to be processed using dimensionality reduction techniques. These techniques include: Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). This reduces the amount of memory and computing power required, while still accurately maintaining original data characteristics. + * **Feature creation** refers to the creation of new features from existing data to help with better predictions. Examples of feature creation include: one-hot-encoding, binning, splitting, and calculated features. @@ -21 +21 @@ Feature engineering is a process to select and transform variables when creating - * **Feature selection** is the process of selecting a subset of extracted features. This is the subset that is relevant and contributes to minimizing the error rate of a trained model. Feature importance score and correlation matrix can be factors in selecting the most relevant features for model training. + * **Feature extraction** involves reducing the amount of data to be processed using dimensionality reduction techniques. These techniques include: Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). This reduces the amount of memory and computing power required, while still accurately maintaining original data characteristics.