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scikit-learn 1.3.2
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# `sklearn.pipeline`.make_pipeline

sklearn.pipeline.make_pipeline(*steps, memory=None, verbose=False)

  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:
    *steps list of Estimator objects

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

    memory str 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.

    verbose bool, default=False

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

  Returns:
    p Pipeline

      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 1.1

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 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 0.22

Release Highlights for scikit-learn 0.22

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

Principal Component Regression vs Partial Least Squares Regression

Principal Component Regression vs Partial Least Squares Regression

Categorical Feature Support in Gradient Boosting

Categorical Feature Support in Gradient Boosting

Combine predictors using stacking

Combine predictors using stacking

Feature transformations with ensembles of trees

Feature transformations with ensembles of trees

Time-related feature engineering

Time-related feature engineering

Model-based and sequential feature selection

Model-based and sequential feature selection

Pipeline ANOVA SVM

Pipeline ANOVA SVM

Univariate Feature Selection

Univariate Feature Selection

Comparing Linear Bayesian Regressors

Comparing Linear Bayesian Regressors

Lasso model selection via information criteria

Lasso model selection via information criteria

Lasso model selection: AIC-BIC / cross-validation

Lasso model selection: AIC-BIC / cross-validation

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

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

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Polynomial and Spline interpolation

Polynomial and Spline interpolation

Robust linear estimator fitting

Robust linear estimator fitting

Tweedie regression on insurance claims

Tweedie regression on insurance claims

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models

Partial Dependence and Individual Conditional Expectation Plots

Partial Dependence and Individual Conditional Expectation Plots

Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

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

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

Advanced Plotting With Partial Dependence

Advanced Plotting With Partial Dependence

Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets

Displaying Pipelines

Displaying Pipelines

Displaying estimators and complex pipelines

Displaying estimators and complex pipelines

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

Introducing the set_output API

Introducing the set_output API

Visualizations with Display Objects

Visualizations with Display Objects

Imputing missing values before building an estimator

Imputing missing values before building an estimator

Imputing missing values with variants of IterativeImputer

Imputing missing values with variants of IterativeImputer

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve

Precision-Recall

Precision-Recall

Approximate nearest neighbors in TSNE

Approximate nearest neighbors in TSNE

Dimensionality Reduction with Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

Varying regularization in Multi-layer Perceptron

Varying regularization in Multi-layer Perceptron

Comparing Target Encoder with Other Encoders

Comparing Target Encoder with Other Encoders

Feature discretization

Feature discretization

Importance of Feature Scaling

Importance of Feature Scaling

Target Encoder’s Internal Cross fitting

Target Encoder's Internal Cross fitting

Clustering text documents using k-means

Clustering text documents using k-means