MLA-C01 Data Preparation for Machine Learning Practice Question

A machine learning team is building a model using a dataset that contains a mix of numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). The team wants to use Amazon SageMaker for training. Which technique should the team use to encode the high-cardinality categorical features effectively?

Answer choices

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Correct answer & explanation

✓

Apply target encoding (mean encoding) to the high-cardinality features.

For high-cardinality categorical features, target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which captures information without creating a large number of dummy variables. One-hot encoding would create too many features. Label encoding implies ordinal relationships. Hash encoding can cause collisions.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Apply hash encoding to map categories to a fixed number of buckets.
Why it's wrong here
Hash encoding may cause collisions and lose information.
✓
Apply target encoding (mean encoding) to the high-cardinality features.
Why this is correct
Target encoding reduces dimensionality and captures target-related information.
✗
Apply one-hot encoding to all categorical features.
Why it's wrong here
One-hot encoding high-cardinality features creates too many columns, increasing dimensionality.
✗
Apply label encoding to assign integer values to each category.
Why it's wrong here
Label encoding implies an order that may not exist, leading to misleading patterns.

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MLA-C01 Data Preparation for Machine Learning Practice Question

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