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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
  1. Clone this repository:
$ git clone https://github.com/pdil/ClassifierKit.git
  1. 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

Datasets