Showing 14 open source projects for "python feature selection"

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  • 1
    tsfresh

    tsfresh

    Automatic extraction of relevant features from time series

    tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python....
    Downloads: 2 This Week
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  • 2
    TextDistance

    TextDistance

    Compute distance between sequences

    Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature.
    Downloads: 0 This Week
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  • 3
    DomainBed

    DomainBed

    DomainBed is a suite to test domain generalization algorithms

    DomainBed is a PyTorch-based research suite created by Facebook Research for benchmarking and evaluating domain generalization algorithms. It provides a unified framework for comparing methods that aim to train models capable of performing well across unseen domains, as introduced in the paper In Search of Lost Domain Generalization. The library includes a wide range of well-known domain generalization algorithms, from classical baselines such as Empirical Risk Minimization (ERM) and...
    Downloads: 0 This Week
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  • 4
    TorBot

    TorBot

    Dark Web OSINT Tool

    Contributions to this project are always welcome. To add a new feature fork the dev branch and give a pull request when your new feature is tested and complete. If its a new module, it should be put inside the modules directory. The branch name should be your new feature name in the format <Feature_featurename_version(optional)>. On Linux platforms, you can make an executable for TorBot by using the install.sh script. You will need to give the script the correct permissions using chmod +x...
    Downloads: 0 This Week
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  • 5
    Digraph3

    Digraph3

    A collection of python3 modules for Algorithmic Decision Theory

    This collection of Python3 modules provides a large range of implemented decision aiding algorithms useful in the field of outranking digraphs based Multiple Criteria Decision Aid (MCDA), especially best choice, linear ranking and absolute or relative rating algorithms with multiple incommensurable criteria. Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs,...
    Downloads: 3 This Week
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  • 6
    Sorting-Visualizer

    Sorting-Visualizer

    A GUI sorting visualizer desktop application

    A GUI sorting visualizer desktop application that helps to visualize various sorting algorithms interactively. Visualizer the sorting algorithms like Bubble sort, Insertion sort, Selection sort, Gnome sort, Shaker sort and Odd even sort. Change the bar color and background by customizing. Increase or decrease speed of animation to visualize the sorting process. Download now!
    Downloads: 0 This Week
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  • 7
    KaHaShEr

    KaHaShEr

    File checksum generator and verifier.

    KaHaShEr is an application to check the integrity of a file using MD5, SHA1, SHA256 and SHA512 checksum. Designed to be smart and easy to use, it includes a feature that can detect a hexadecimal value in the clipboard and automatically perform the verification while notifying you with a very expressive sound of the outcome of this verification.
    Downloads: 0 This Week
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  • 8
    Active Learning

    Active Learning

    Framework and examples for active learning with machine learning model

    ...The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks.
    Downloads: 3 This Week
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  • 9
    zCharter
    Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
    Downloads: 0 This Week
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  • 10

    Distant Speech Recognition

    Beamforming and Speech Recognition Toolkit

    BTK contains C++ and Python libraries that implement speech processing and microphone array techniques such as speech feature extraction, speech enhancement, speaker tracking, beamforming, dereverberation and echo cancellation algorithms. The Millennium ASR provides C++ and python libraries for automatic speech recognition. The Millennium ASR implements a weighted finite state transducer (WFST) decoder, training and adaptation methods.
    Downloads: 1 This Week
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  • 11
    Modular toolkit for Data Processing MDP
    ...The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
    Downloads: 7 This Week
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  • 12
    Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...
    Downloads: 1 This Week
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  • 13
    MRA

    MRA

    A general recommender system with basic models and MRA

    Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide...
    Downloads: 0 This Week
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  • 14
    Cluster Analysis Wrapped Feature Subset Selection (CAWFSS) is a collection of feature subset selection algorithms which use various cluster analysis algorithms as wrappers.
    Downloads: 0 This Week
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