sklearn.feature_extraction.DictVectorizer

DictVectorizer is a feature extraction tool in sklearn library that converts a dictionary of feature-value mappings into a feature matrix. It is used to transform a list of feature-value dictionaries into a sparse matrix, where each column represents a unique feature and each row represents an instance. This is a common preprocessing step before feeding the data to a machine learning algorithm.

The DictVectorizer takes an iterable of dictionaries as input and returns a sparse matrix. The feature-value mappings can be of any type, but the values should be numerical or categorical. The categorical values are automatically converted into numerical values using one-hot encoding. This means that one column is created for each unique value of the categorical feature, and the value of the column is either 0 or 1 depending on whether the instance has that value for the feature.

The DictVectorizer can also handle missing values by imputing them with a default value, which can be specified using the "missing_value" parameter. Additionally, it supports feature scaling using the "dtype" parameter, which can be set to float32 or float64.

Overall, the DictVectorizer is a useful tool for converting a list of dictionaries into a feature matrix that can be used for machine learning tasks.