make_pipeline

sklearn.pipeline.make_pipeline(*steps, memory=None, transform_input=None, verbose=False)[source]

Construct a Pipeline from the given estimators.

This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.

Parameters:

*stepslist of Estimator objects

List of the scikit-learn estimators that are chained together.

memorystr or object with the joblib.Memory interface, default=None

Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute named_steps or steps to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.

transform_inputlist of str, default=None

This enables transforming some input arguments to fit (other than X) to be transformed by the steps of the pipeline up to the step which requires them. Requirement is defined via metadata routing. This can be used to pass a validation set through the pipeline for instance.

You can only set this if metadata routing is enabled, which you can enable using sklearn.set_config(enable_metadata_routing=True).

Added in version 1.6.

verbosebool, default=False

If True, the time elapsed while fitting each step will be printed as it is completed.

Returns:

pPipeline

Returns a scikit-learn Pipeline object.

See also

Pipeline

Class for creating a pipeline of transforms with a final estimator.

Examples

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.pipeline import make_pipeline
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('gaussiannb', GaussianNB())])

Gallery examples

Time-related feature engineering

Analysis of the convergence of penalized logistic regression models

Plot classification probability

Classifier comparison

A demo of K-Means clustering on the handwritten digits data

Principal Component Regression vs Partial Least Squares Regression

Feature transformations with ensembles of trees

Categorical Feature Support in Gradient Boosting

Combine predictors using stacking

Visualizing the probabilistic predictions of a VotingClassifier

Univariate Feature Selection

Pipeline ANOVA SVM

Model-based and sequential feature selection

Imputing missing values with variants of IterativeImputer

Imputing missing values before building an estimator

Common pitfalls in the interpretation of coefficients of linear models

Partial Dependence and Individual Conditional Expectation Plots

Advanced Plotting With Partial Dependence

Scalable learning with polynomial kernel approximation

Comparing Linear Bayesian Regressors

Lasso model selection via information criteria

Lasso model selection: AIC-BIC / cross-validation

Regularization path of L1- Logistic Regression

Poisson regression and non-normal loss

Polynomial and Spline interpolation

Robust linear estimator fitting

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

Tweedie regression on insurance claims

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…

Comparing anomaly detection algorithms for outlier detection on toy datasets

Visualizations with Display Objects

Displaying estimators and complex pipelines

Evaluation of outlier detection estimators

Displaying Pipelines

Introducing the set_output API

Post-tuning the decision threshold for cost-sensitive learning

Detection error tradeoff (DET) curve

Precision-Recall

Post-hoc tuning the cut-off point of decision function

Approximate nearest neighbors in TSNE

Dimensionality Reduction with Neighborhood Components Analysis

Varying regularization in Multi-layer Perceptron

Feature discretization

Importance of Feature Scaling

Comparing Target Encoder with Other Encoders

Target Encoder’s Internal Cross fitting

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.7

Release Highlights for scikit-learn 1.8

Release Highlights for scikit-learn 1.9

Clustering text documents using k-means