Which feature engineering process should your team apply to select a subset of features that are the most relevant towards minimizing the error rate of the trained model?

Your data science team is working on developing a machine learning model to predict customer churn. The dataset that you are using contains hundreds of features, but you suspect that not all of these features are equally important for the model’s accuracy. To improve the model’s performance and reduce its complexity, the team wants to focus on selecting only the most relevant features that contribute significantly to minimizing the model’s error rate.

Which feature engineering process should your team apply to select a subset of features that are the most relevant towards minimizing the error rate of the trained model?
A . Feature extraction
B . Feature creation
C . Feature transformation
D . Feature selection

View Answer

Answer: D

Explanation:

Correct option:

Feature selection

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.

Incorrect options:

Feature creation – 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 transformation – Feature transformation and imputation include steps for replacing missing features or features that are not valid. Some techniques include: forming Cartesian products of features, non-linear transformations (such as binning numeric variables into categories), and creating re 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).

Reference: https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/feature-engineering.html