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This article describes the modules in Machine Learning Studio (classic) that support the creation of classification models. You can use these modules to build binary or multiclass classification models.
Applies to: Machine Learning Studio (classic) only
Similar drag-and-drop modules are available in Azure Machine Learning designer.
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data. For example, you can use classification to:
- Classify email filters as spam, junk, or good.
- Determine whether a patient's lab sample is cancerous.
- Categorize customers by their propensity to respond to a sales campaign.
- Identify sentiment as positive or negative.
Classification tasks are frequently organized by whether a classification is binary (either A or B) or multiclass (multiple categories that can be predicted by using a single model).
Create a classification model
To create a classification model, or classifier, first, select an appropriate algorithm. Consider these factors:
- How many classes or different outcomes do you want to predict?
- What is the distribution of the data?
- How much time can you allow for training?
Machine Learning Studio (classic) provides multiple classification algorithms. When you use the One-Vs-All algorithm, you can even apply a binary classifier to a multiclass problem.
After you choose an algorithm and set the parameters by using the modules in this section, train the model on labeled data. Classification is a supervised machine learning method. It always requires labeled training data.
When training is finished, you can evaluate and tune the model. When you're satisfied with the model, use the trained model for scoring with new data.
List of modules
The Classification category includes the following modules:
- Multiclass Decision Forest: Creates a multiclass classification model by using the decision forest algorithm.
- Multiclass Decision Jungle: Creates a multiclass classification model by using the decision jungle algorithm.
- Multiclass Logistic Regression: Creates a multiclass logistic regression classification model.
- Multiclass Neural Network: Creates a multiclass classification model by using a neural network algorithm.
- One-vs-All Multiclass: Creates a multiclass classification model from an ensemble of binary classification models.
- Two-Class Averaged Perceptron: Creates an averaged perceptron binary classification model.
- Two-Class Bayes Point Machine: Creates a Bayes point machine binary classification model.
- Two-Class Boosted Decision Tree: Creates a binary classifier by using a boosted decision tree algorithm.
- Two-Class Decision Forest: Creates a two-class classification model by using the decision forest algorithm.
- Two-Class Decision Jungle: Creates a two-class classification model by using the decision jungle algorithm.
- Two-Class Locally Deep Support Vector Machine: Creates a binary classification model by using the locally deep Support Vector Machine algorithm.
- Two-Class Logistic Regression: Creates a two-class logistic regression model.
- Two-Class Neural Network: Creates a binary classifier by using a neural network algorithm.
- Two-Class Support Vector Machine: Creates a binary classification model by using the Support Vector Machine algorithm.
For examples of classification in action, see the Azure AI Gallery.
For help choosing an algorithm, see these articles:
Machine learning algorithm cheat sheet for Machine Learning
Provides a graphical decision chart to guide you through the selection process.
Choose Machine Learning algorithms for clustering, classification, or regression
Explains in greater detail the different types of machine learning algorithms and how they're used.