Learn the best approach for an ML engineer to predict the contribution of each feature in a training dataset before selecting features for a risk analysis machine learning model. Discover how to use Amazon SageMaker Data Wrangler to evaluate feature importance.
Table of Contents
Question
A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features.
How should the ML engineer predict the contribution of each feature?
A. Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.
B. Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.
C. Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering.
D. Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline. Manually add the feature scores.
Answer
A. Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.
Explanation
Multicollinearity measures the correlation between features in a dataset. High multicollinearity means features are highly correlated, which can negatively impact model performance. SageMaker Data Wrangler’s multicollinearity features help identify redundant features.
PCA is a dimensionality reduction technique that transforms the feature space to capture the maximum variance in the data using a smaller number of orthogonal components. It helps identify which combinations of features explain the most variation.
By using multicollinearity analysis and PCA together, the ML engineer can understand the relationships between features and how much each one contributes to the overall variance in the dataset. This enables them to select the most informative features that will lead to better model performance.
The other options are incorrect:
- Quick model visualizations and feature importance scores between 0.5 and 1 are arbitrary and don’t provide a principled way to evaluate features.
- Bias reports help identify potential bias issues but don’t directly measure feature contribution.
- Manually adding feature scores in a data flow is subjective and error-prone compared to using analytical techniques.
In summary, using Amazon SageMaker Data Wrangler’s multicollinearity features along with PCA is the best approach for the ML engineer to predict the contribution of each feature before feature selection. This will optimize the risk analysis model’s performance.
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