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Lære Challenge: Creating a Complete ML Pipeline | Pipelines
ML Introduction with scikit-learn
course content

Kursinnhold

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
2. Preprocessing Data with Scikit-learn
3. Pipelines
4. Modeling

book
Challenge: Creating a Complete ML Pipeline

Now let's create a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.

To train a predictor (model), you need y to be encoded. This is done separately from the pipeline we build for X. Remember that LabelEncoder is used for encoding the target.

Oppgave

Swipe to start coding

You have the same penguins dataset. The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.

  1. Encode the y variable.
  2. Create a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier.
  3. Train the pipe object using the features X and the target y.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 3. Kapittel 6
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book
Challenge: Creating a Complete ML Pipeline

Now let's create a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.

To train a predictor (model), you need y to be encoded. This is done separately from the pipeline we build for X. Remember that LabelEncoder is used for encoding the target.

Oppgave

Swipe to start coding

You have the same penguins dataset. The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.

  1. Encode the y variable.
  2. Create a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier.
  3. Train the pipe object using the features X and the target y.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 3. Kapittel 6
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Vi beklager at noe gikk galt. Hva skjedde?
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