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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.

Naive Bayesian Classifer in APL Alternatives

The best Naive Bayesian Classifer in APL alternatives based on verified products, community votes, reviews and other factors.
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  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.

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

    Open Source

  3. Clear. Fast. Unlimited. Residential & Mobile Proxies For Best Price .

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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.

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

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

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

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

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

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

  11. OpenAI GYM is a toolkit developers use to both develop and compare reinforcement learning algorithms. Their GitHub repository includes dozens of contributors... read more.

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

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

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

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