sklearn.pipeline.make_pipeline

sklearn.pipeline.make_pipeline(*steps, memory=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. 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.

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())])

Examples using sklearn.pipeline.make_pipeline

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2
Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1
Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.0
Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24
Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23
Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22
Classifier comparison

Classifier comparison

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

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

A demo of K-Means clustering on the handwritten digits data
Principal Component Regression vs Partial Least Squares Regression

Principal Component Regression vs Partial Least Squares Regression

Principal Component Regression vs Partial Least Squares Regression
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Combine predictors using stacking

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Feature transformations with ensembles of trees

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Time-related feature engineering

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Pipeline ANOVA SVM

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Univariate Feature Selection

Univariate Feature Selection

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Comparing Linear Bayesian Regressors

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Lasso model selection via information criteria

Lasso model selection via information criteria

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Lasso model selection: AIC-BIC / cross-validation

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

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One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
Poisson regression and non-normal loss

Poisson regression and non-normal loss

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Polynomial and Spline interpolation

Polynomial and Spline interpolation

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Robust linear estimator fitting

Robust linear estimator fitting

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Tweedie regression on insurance claims

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Common pitfalls in the interpretation of coefficients of linear models

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Partial Dependence and Individual Conditional Expectation Plots

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Comparing anomaly detection algorithms for outlier detection on toy datasets

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Displaying Pipelines

Displaying Pipelines

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Displaying estimators and complex pipelines

Displaying estimators and complex pipelines

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Introducing the `set_output` API

Introducing the set_output API

Introducing the `set_output` API
Visualizations with Display Objects

Visualizations with Display Objects

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Imputing missing values before building an estimator

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Imputing missing values before building an estimator
Imputing missing values with variants of IterativeImputer

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Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve
Precision-Recall

Precision-Recall

Precision-Recall
Approximate nearest neighbors in TSNE

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Dimensionality Reduction with Neighborhood Components Analysis

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Varying regularization in Multi-layer Perceptron

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Varying regularization in Multi-layer Perceptron
Feature discretization

Feature discretization

Feature discretization
Importance of Feature Scaling

Importance of Feature Scaling

Importance of Feature Scaling
Clustering text documents using k-means

Clustering text documents using k-means

Clustering text documents using k-means