[Concept Entry] Sklearn multilabel-classification

[SaviDahegaonkar]

Description

Created a new entry on the Multilabel Classification concept under Sklearn.

Issue Solved

Closes #4967

Type of Change

• Adding a new entry

Checklist

• All writings are my own.
• My entry follows the Codecademy Docs style guide.
• My changes generate no new warnings.
• I have performed a self-review of my own writing and code.
• I have checked my entry and corrected any misspellings.
• I have made corresponding changes to the documentation if needed.
• I have confirmed my changes are not being pushed from my forked main branch.
• I have confirmed that I'm pushing from a new branch named after the changes I'm making.
• I have linked any issues that are relevant to this PR in the Issues Solved section.

[mamtawardhani]

Thank you for contributing to Codecademy Docs @SaviDahegaonkar 😄

The entry looks good to be merged! 🚀

[mamtawardhani]

Suggested change

	Description: 'Multilabel classification assigns multiple labels to a single instance, using scikit-learn tools and models.'
	Description: 'Multilabel classification is a machine learning task where each instance can be assigned multiple labels or categories simultaneously.'

[mamtawardhani]

Suggested change

	In Sklearn, **Multilabel classification** assigns multiple labels to a single instance using scikit-learn tools and models. This method differs from traditional classification, where each instance belongs to one class, and this technique predicts multiple outputs simultaneously.
	Scikit-learn offers tools like `OneVsRestClassifier`, `ClassifierChain`, and `MultiOutputClassifier` to facilitate multilabel classification and enable efficient model training and evaluation.
	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.

[mamtawardhani]

Suggested change

	from sklearn.multioutput import MultiOutputClassifier
	from sklearn.ensemble import RandomForestClassifier
	# 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)
	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)

[mamtawardhani]

Suggested change

	- `RandomForestClassifier`: Used as the base estimator, and can be replaced with any scikit-learn classifier.
	- `MultiOutputClassifier`: Wraps the base model to handle multiple output labels.
	- `Fit`: Method used to train the model on training data.
	- `predict()`: Method makes predicitions on the test data.
	- `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()`.

[mamtawardhani]

Suggested change

	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 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)
	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)

[mamtawardhani]

Suggested change

	Predicted Labels for First 5 Samples:
	[[1 1 1]
	[1 1 0]
	[1 1 1]
	[1 1 0]
	[0 1 0]]
	Predicted Labels for First 5 Samples:
	[[1 1 0]
	[1 1 0]
	[0 0 1]
	[1 1 1]
	[0 1 0]]

[github-actions]

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