- Input: List of dictionaries (
[{feature: value, ...}, ...]). - Method: Transforms dictionaries into a numeric feature matrix.
- Best for: When your data is naturally represented as
key-valuepairs or you need integration with machine learning pipelines like Scikit-learn. - Example Use Case: Encoding features from unstructured or JSON-like data sources (e.g., API responses) into a format suitable for ML models.
- What It Does:
- Converts a list of dictionaries (key-value pairs) into a matrix where keys become feature names.
- Primarily designed for machine learning workflows where the data is in a dictionary-like structure.
- Can produce sparse matrices, which is memory efficient for large datasets.
- When to Use:
- When working with dictionary-style data (
[{feature: value, ...}, ...]) instead of pandas DataFrames. - When you need to integrate with Scikit-learn pipelines or other ML workflows.
- Ideal for large datasets or when working with sparse data (e.g., many features with mostly zero values).
- Pros:
- Handles dictionary-style inputs seamlessly.
- Supports sparse representations for memory efficiency.
- Integrates well with
Scikit-learn'spipeline. - Cons:
- Requires converting pandas DataFrames to dictionary format first, which can be tedious.
- Slightly less intuitive thanĀ
pd.get_dummies()Ā for pandas users.
DictVectorizer
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