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Naive Bayesian Classifer in APL

Naive Bayesian Classifer in APL is a simple naive bayesian classifier to gain independent probabilistic assumptions on test input.

Best Naive Bayesian Classifer in APL Alternatives & Competitors in 2024

The best Naive Bayesian Classifer in APL alternatives based on verified products, community votes, reviews and other factors.
Filter: 8 Open-Source Alternatives. Latest update:

  1. htm.java is a Hierarchical Temporal Memory implementation in Java, it provide a Java version of NuPIC that has a 1-to-1 correspondence to all systems, functionality and tests provided by Numenta's open source implementation.

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  2. scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

    Open Source

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  4. Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

    Open Source

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  5. NumPy is the fundamental package for scientific computing with Python

    Open Source

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  6. OpenCV is the world's biggest computer vision library

    Open Source

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  7. Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.

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  8. Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.

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  9. Figure Eight is the essential Human-in-the-Loop Machine Learning platform.

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  10. WEKA is a set of powerful data mining tools that run on Java.

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  11. GraphLab Create is an extensible machine learning framework that enables developers and data scientists to easily build and deploy apps.

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  12. The DimML programming language enables users to run any data solution on any website with only a single line of code.

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  13. Logical Glue helps Lenders and Insurance organisations make better decisions with a highly intuitive and user-friendly Machine Learning Platform.

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  14. RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.

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