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Model Selection

Scope of This Project

  • As a beginner in Data science/Machine learning field, most of us having issue in Feature Selection, Feature Extraction and Model Selection. Algorithm-Finder can help you to solve this problem. using Algorithm-Finder you can achieve the below tasks and more.

    • Model Selection
    • Feature Selection
    • Feature Extraction
    • Optimized tuning parameters
  • This package mainly used scikit-learn for most of the estimators, by using Algorithm-Finder you can apply your dataset on below models

    • ALL --> ALL IN
    • MLR --> MultiLinearRegression
    • POLY --> PolynomialRegression
    • SVR --> SupportVectorRegression
    • DTREE --> DecisionTreeRegression & DecisionTreeClassification
    • RFR --> RandomForestRegression
    • SIGMOID --> LogisticRegression
    • KNN --> K-NearestNeighbours Classifier
    • SVM --> SupportVectorMachine Classifier
    • RFC --> RandomForestClassifier
    • BAYESIAN --> NaiveBayesClassifier

Release files for model-selection 0.0.1

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Source distribution (sdist)

Source distribution for model-selection 0.0.1
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Table of built distributions (wheels) for model-selection 0.0.1
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model_selection-0.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 21.1 kB

Release files / model_selection-0.0.1.tar.gz

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Release files / model_selection-0.0.1-py3-none-any.whl

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0.0.1 This release

2 release files

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