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ML Introduction with scikit-learn
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Kursusindhold

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow
2. Preprocessing Data with Scikit-learn
Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesChallenge: Imputing Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderChallenge: Encoding Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerChallenge: Scaling the Features
3. Pipelines
What is PipelineColumnTransformerEfficient Data Preprocessing with PipelinesChallenge: Creating a PipelineFinal EstimatorChallenge: Creating a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating the ModelCross-ValidationChallenge: Evaluating the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVChallenge: Tuning Hyperparameters with RandomizedSearchCVModeling SummaryChallenge: Putting It All Together

book
Challenge: Creating a Pipeline

In this challenge, you need to put all the preprocessing steps we did together into one pipeline. The dataset is the initial penguins.csv file we started from.

The first step is to remove two useless rows. Then you will have to create a pipeline containing encoding, imputing, and scaling.

You need to encode only two columns, 'sex' and 'island'. Since you do not want to encode the entire X, you must use a ColumnTransformer. Afterward, apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Opgave

Swipe to start coding

  1. Import the correct function for creating a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Make sure that all other columns remain untouched.
  4. Create a pipeline containing ct you just created, SimpleImputer that fills in missing values with the most frequent value and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 4
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book
Challenge: Creating a Pipeline

In this challenge, you need to put all the preprocessing steps we did together into one pipeline. The dataset is the initial penguins.csv file we started from.

The first step is to remove two useless rows. Then you will have to create a pipeline containing encoding, imputing, and scaling.

You need to encode only two columns, 'sex' and 'island'. Since you do not want to encode the entire X, you must use a ColumnTransformer. Afterward, apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Opgave

Swipe to start coding

  1. Import the correct function for creating a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Make sure that all other columns remain untouched.
  4. Create a pipeline containing ct you just created, SimpleImputer that fills in missing values with the most frequent value and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 4
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Vi beklager, at noget gik galt. Hvad skete der?
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