Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Learn Challenge: Creating a Complete ML Pipeline | Pipelines
ML Introduction with scikit-learn

Swipe to show menu

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.

Task

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.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 6
We're sorry to hear that something went wrong. What happened?

Ask AI

expand
ChatGPT

Ask anything or try one of the suggested questions to begin our chat

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.

Task

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.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt