Multi-Label Classification Example with MultiOutputClassifier and XGBoost in Python

  1. Preparing the data
  2. Defining the model
  3. Predicting and accuracy check
  4. Source code listing

We’ll start by loading the required libraries for this tutorial.

Preparing the data

x, y = make_multilabel_classification(n_samples=10000, n_features=20, n_classes=5, random_state=88)

Next, we’ll split the data into the train and test parts.

xtrain, xtest, ytrain, ytest=train_test_split(x, y, train_size=0.8, random_state=88)

Defining the model

We’ll fit the model with training data and check the training accuracy.

clf.fit(xtrain, ytrain)
print(clf.score(xtrain, ytrain))

Source code listing

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