Feature Extraction

<aside> 💡 sklearn.feature_extraction

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DictVectorizer

Converts lists of mappings of feature name and feature value, into a matrix.

FeatureHasher

• High-speed, low-memory vectorizer that uses feature hashing technique.
• Instead of building a hash table of the features, as the vectorizers do, it applies a hash function to the features to determine their column index in sample matrices directly.
• This results in increased speed and reduced memory usage, at the expense of inspectability; the hasher does not remember what the input features looked like and has no inverse_transform method.
• The output is scipy.sparse matrix.

Data Cleaning

Handling missing values

<aside> 💡 sklearn.impute

</aside>

SimpleImputer

Fills missing values with one of the following strategies: 'mean', 'median', 'most_frequent' and 'constant'.

KNNImputer

• Uses the k-nearest neighbours approach to fill missing values in a dataset.
• The missing value of an attribute in a specific example is filled with the mean value of the same attribute of n_neighbors closest neighbours.
• The nearest neighbours are decided based on Euclidean distance.