ClassifierKit

🤖 A suite of tools and examples for training Core ML models with Create ML.

📄 Requirements

• macOS 10.14 (Mojave) or later (download)
• Xcode 10 or later (download)
• Swift 4.2 or later

Important Note: Create ML is not available for the iOS SDK. It can only be used on macOS to train models and is not intended for on-device training. Instead, it is used to train models with data (which may take minutes to hours depending on computing power, size of dataset, and model). When the model is trained, a .mlmodel file can be exported and implemented in iOS/tvOS/watchOS/macOS apps using Core ML.

⚙️ Models

The following models are available as example Playgrounds:

Model	Associated Type	Playground
🌅 Image Classifier	MLImageClassifier
🌅 Image Classifier Builder	MLImageClassifierBuilder	🔗
📄 Text Classifier	MLTextClassifier	🔗
🏷️ Word Tagger	MLWordTagger	🔗
📊 Decision Tree Classifier	MLDecisionTreeClassifier	🔗
📊 Random Forest Classifier	MLRandomForestClassifier	🔗
📊 Boosted Tree Classifier	MLBoostedTreeClassifier
📊 Logistic Regression Classifier	MLLogisticRegressionClassifier
📊 Support Vector Classifier	MLSupportVectorClassifier
📈 Linear Regressor	MLLinearRegressor
📈 Decision Tree Regressor	MLDecisionTreeRegressor
📈 Boosted Tree Regressor	MLBoostedTreeRegressor
📈 Random Forest Regressor	MLRandomForestRegressor

Note: Some of these are incomplete and are currently being added. The goal is to eventually have comprehensive example playgrounds for each model type in Create ML, including sample data and explanations. See Project #1 to track the progress of these playgrounds.

📝 Usage

The easiest way to begin using ClassifierKit is to clone it directly onto your computer.

1. Navigate to the desired directory on your local filesystem.

$ cd Desktop/or/any/other/folder

2. Clone this repository:

$ git clone https://github.com/pdil/ClassifierKit.git

3. Begin! The Playgrounds folder contains Swift Playgrounds for the many models contained within Create ML that will allow you to set parameters and begin training the models, either with the provided sample data or your own data.

🗃️ References

• Introducing Create ML · WWDC 2018 · Session 703
• Create ML · Apple Developer Documentation

Datasets

• Mushroom Data Set · UCI Machine Learning Repository