smile OSS for machine learning engine


Smile is a machine learning library written in Java. It is mainly used to perform machine learning tasks such as classification, regression, clustering, dimensionality reduction, and association rule learning.


Smile provides a simple and easy-to-use API with rich features. It implements many machine learning algorithms and supports tasks from data preprocessing to model training and evaluation.


Smile offers various algorithms such as Bayesian optimization, anomaly detection, decision trees, random forests, support vector machines, neural networks, and more. It also supports time series analysis and natural language processing.

How to Use

Since Smile is a library written in Java, it is used when building machine learning models using the Java programming language. You can program the steps of data loading, preprocessing, model training, and evaluation yourself.

Github URL:

haifengl/smile OSS is a Java library for machine learning and data mining. This library can be used for various machine learning tasks such as classification, regression, clustering, dimensionality reduction, and data preprocessing.

haifengl/smile OSS is utilized in software architecture to develop analysis and prediction models. It is effectively used in applications such as customer segmentation, market analysis using large datasets, image recognition, and natural language processing.

Benefits of using haifengl/smile OSS include:

  1. Integration with Java-based development environments is easy due to being written in Java.
  2. It provides fast and efficient algorithms, enabling real-time prediction and analysis.
  3. The library has comprehensive documentation with sample code and tutorials for various tasks, making it easy for beginners to use.

For these reasons, haifengl/smile OSS is widely used in the fields of machine learning and data mining, adopted by many companies and research institutions.



  1. smile-core:

    • The core package of Smile, containing basic data structures and algorithms. This package implements machine learning algorithms such as utility methods, random forests, k-nearest neighbors, k-means, and more.
  2. smile-data:

    • Contains utility methods for loading and exporting datasets. It is mainly used for data preprocessing and format conversion.
  3. smile-io:

    • Includes utility methods for reading data from files. It implements methods for handling data in CSV and JSON formats.
  4. smile-graph:

    • Contains methods related to graph algorithms. It is used for graph-related processing such as shortest path finding and clustering.
  5. smile-nlp:

    • Includes methods related to natural language processing. It implements tasks like text tokenization, word vector representation, and document classification.

GitHub URL

Smile provides a range of useful machine learning algorithms and data processing methods, effectively supporting data science and machine learning projects. Each package implements functionality specialized for specific tasks, offering flexibility and ease of use.

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