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ML Introduction with scikit-learn
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Cursusinhoud

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

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Challenge: Evaluating the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation. The data is an already preprocessed penguins dataset.

Here are some of the functions you will use:

Taak

Swipe to start coding

Your task is to create a 4-nearest neighbors classifier and first evaluate its performance using the cross-validation score. Then split the data into train-test sets, train the model on the training set, and evaluate its performance on the test set.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. Calculate the cross-validation scores of this model with the number of folds set to 3. You can pass an untrained model to a cross_val_score() function.
  3. Use a suitable function to split X, y.
  4. Train the model using the training set.
  5. Evaluate the model using the test set.

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 4. Hoofdstuk 5
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book
Challenge: Evaluating the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation. The data is an already preprocessed penguins dataset.

Here are some of the functions you will use:

Taak

Swipe to start coding

Your task is to create a 4-nearest neighbors classifier and first evaluate its performance using the cross-validation score. Then split the data into train-test sets, train the model on the training set, and evaluate its performance on the test set.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. Calculate the cross-validation scores of this model with the number of folds set to 3. You can pass an untrained model to a cross_val_score() function.
  3. Use a suitable function to split X, y.
  4. Train the model using the training set.
  5. Evaluate the model using the test set.

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 4. Hoofdstuk 5
Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
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