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Predicting mushroom edibility

Now that you have explored the data, it's time to build a first model to predict mushroom edibility.

The dataset is available to you as mushrooms. As both the features and the target are categorical, these have been transformed into "dummy" binary variables for you.

Let's begin with Naive Bayes (using scikit-learn's GaussianNB) and see how this algorithm performs on this problem.

Deze oefening maakt deel uit van de cursus

Ensemble Methods in Python

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Oefeninstructies

  • Instantiate a GaussianNB classifier called clf_nb.
  • Fit clf_nb to the training data X_train and y_train.
  • Calculate the predictions on the test set. These predictions will be used to evaluate the performance using the accuracy score.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Instantiate a Naive Bayes classifier
clf_nb = ____

# Fit the model to the training set
____

# Calculate the predictions on the test set
pred = ____

# Evaluate the performance using the accuracy score
print("Accuracy: {:0.4f}".format(accuracy_score(y_test, pred)))
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