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Building a logistic regression model

In this exercise, you will build a logistic regression model using all features in the diabetes_df dataset. The model will be used to predict the probability of individuals in the test set having a diabetes diagnosis.

The diabetes_df dataset has been split into X_train, X_test, y_train, and y_test, and preloaded for you.

This exercise is part of the course

Supervised Learning with scikit-learn

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Exercise instructions

  • Import LogisticRegression.
  • Instantiate a logistic regression model, logreg.
  • Fit the model to the training data.
  • Predict the probabilities of each individual in the test set having a diabetes diagnosis, storing the array of positive probabilities as y_pred_probs.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import LogisticRegression
____

# Instantiate the model
logreg = ____

# Fit the model
____

# Predict probabilities
y_pred_probs = logreg.____(____)[____, ____]

print(y_pred_probs[:10])
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