Implementation:Scikit learn Scikit learn EstimatorPrettyPrint

Knowledge Sources	Scikit_learn Scikit-learn Docs
Domains	Machine Learning, Display Formatting
Last Updated	2026-02-08 15:00 GMT

Overview

Concrete utility module for pretty-printing scikit-learn estimator representations provided by scikit-learn.

Description

The _pprint module contains the _EstimatorPrettyPrinter class used in BaseEstimator.__repr__ for pretty-printing estimators. It extends Python's built-in pprint module with support for compact dict rendering, custom estimator handling, and ellipsis-based truncation of long sequences. The module also provides the _changed_params helper to display only non-default parameters.

Usage

This module is used internally by scikit-learn's BaseEstimator.__repr__ method. It is invoked automatically whenever an estimator is printed or displayed in a notebook or terminal.

Code Reference

Source Location

• Repository: scikit-learn
• File: sklearn/utils/_pprint.py

Signature

class KeyValTuple(tuple):
    ...

class KeyValTupleParam(KeyValTuple):
    ...

def _changed_params(estimator):
    ...

class _EstimatorPrettyPrinter(pprint.PrettyPrinter):
    ...

def _safe_repr(object, context, maxlevels, level, changed_only=False):
    ...

Import

from sklearn.utils._pprint import _EstimatorPrettyPrinter

I/O Contract

Inputs

Name	Type	Required	Description
estimator	BaseEstimator	Yes	The estimator to pretty-print
changed_only	bool	No	If True, only show parameters that differ from defaults

Outputs

Name	Type	Description
repr_string	str	A formatted string representation of the estimator

Usage Examples

Basic Usage

from sklearn.linear_model import LogisticRegression

# Pretty printing is automatic via __repr__
lr = LogisticRegression(C=0.5, max_iter=200)
print(lr)
# LogisticRegression(C=0.5, max_iter=200)

Related Pages

• Principle:Scikit_learn_Scikit_learn_Model_Instantiation