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+---
+Title: 'Multilabel Classification'
+Description: 'Multilabel classification is a machine learning task where each instance can be assigned multiple labels or categories simultaneously.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+  - 'Data Visualization'
+  - 'Machine Learning'
+Tags:
+  - 'AI'
+  - 'Classification'
+  - 'Natural Language Processing'
+  - 'Scikit-learn'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/intermediate-machine-learning-skill-path'
+---
+
+In sklearn, **Multilabel Classification** assigns multiple labels to a single instance, allowing models to predict multiple outputs simultaneously. This method differs from traditional classification, where each instance belongs to only one class.
+
+Scikit-learn offers tools like `OneVsRestClassifier`, `ClassifierChain`, and `MultiOutputClassifier` to handle multilabel classification and enable efficient model training and evaluation.
+
+## Syntax
+
+Here's the syntax for using multiabel classification in sklearn:
+
+```pseudo
+from sklearn.multioutput import MultiOutputClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+# Step 1: Initialize the base classifier
+base_model = RandomForestClassifier(random_state=42)
+
+# Step 2: Create a MultiOutputClassifier wrapper for multilabel classification
+multi_label_model = MultiOutputClassifier(base_model)
+
+# Step 3: Train the model using the training dataset
+multi_label_model.fit(X_train, y_train)
+
+# Step 4: Make predictions on the test dataset
+predicted_labels = multi_label_model.predict(X_test)
+
+# Step 5: Evaluate predictions or use the results
+print(predicted_labels)
+```
+
+- `RandomForestClassifier`: The base classifier for multilabel classification.
+- `MultiOutputClassifier`: A wrapper to extend the base classifier for multilabel tasks.
+- `Training and testing`: The model is trained with `fit()` and predictions are made using `predict()`.
+
+## Example
+
+This code demonstrates multilabel classification using scikit-learn by training a model to assign multiple labels:
+
+```py
+from sklearn.datasets import make_multilabel_classification
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.multioutput import MultiOutputClassifier
+from sklearn.metrics import classification_report
+
+# Generate synthetic multilabel data
+X, y = make_multilabel_classification(n_samples=100, n_features=10, n_classes=3, n_labels=2, random_state=42)
+
+# Initialize a base classifier
+base_classifier = RandomForestClassifier()
+
+# Wrap the base classifier for multilabel classification
+model = MultiOutputClassifier(base_classifier)
+
+# Train the model
+model.fit(X, y)
+
+# Predict labels for new data
+predictions = model.predict(X[:5])
+
+# Display predictions
+print("Predicted Labels for First 5 Samples:")
+print(predictions)
+```
+
+The code results the following output:
+
+```shell
+Predicted Labels for First 5 Samples:
+[[1 1 0]
+ [1 1 0]
+ [0 0 1]
+ [1 1 1]
+ [0 1 0]]
+```
+
+## Codebyte Example
+
+The following codebyte example trains a Random Forest classifier for multilabel classification on dataset and predicts multiple categories for new samples:
+
+```codebyte/python
+# This code demonstrates multilabel classification using scikit-learn.
+from sklearn.datasets import make_multilabel_classification
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.multioutput import MultiOutputClassifier
+
+# Generate synthetic multilabel data
+X, y = make_multilabel_classification(n_samples=100, n_features=10, n_classes=3, n_labels=2, random_state=42)
+
+# Initialize a Random Forest classifier
+classifier = RandomForestClassifier()
+
+# Wrap the classifier for multilabel classification
+multi_label_model = MultiOutputClassifier(classifier)
+
+# Train the model on the dataset
+multi_label_model.fit(X, y)
+
+# Predict labels for the first 4 samples
+predictions = multi_label_model.predict(X[:4])
+
+# Display the predictions
+print("Predicted labels for the first 4 samples:")
+print(predictions)
+```