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Model performance

You're now going to evaluate the model from the previous lesson against the test-data.

Evaluating data against new, unseen data is important, as it proves the ability of the model to correctly estimate data it has never encountered before.

All necessary modules have been imported, and the data is available as X_train and y_train, and X_test and y_test respectively.

Cet exercice fait partie du cours

Analyzing IoT Data in Python

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Instructions

  • Create a LogisticRegression model.
  • Fit the model to X_train and y_train.
  • Score the model using X_train and y_train.
  • Score the model using X_test and y_test.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Create LogisticRegression model
logreg = ____()

# Fit the model
logreg.____(____, ____)

# Score the model
print(logreg.____(____, ____))
print(____)
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