SmartPredict
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SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
      • Web Services
      • Data retrieval
      • Data preprocessing
      • Data selection
      • Training and Prediction
      • Evaluation and fine-tuning
      • Machine Learning algorithms
        • ML modules in SmartPredict
        • Decision Tree Regressor
        • KNeighbors Classifier
        • KNeighbors Regressors
        • Linear Regressor
        • Logistic Regressor
        • MLP Regressor
        • Naive Bayes Classifier
        • Random Forest Classifier
        • Random Forest Regressor
        • Support Vector Classifier
        • Support Vector Regressor
        • XGBoost Classifier
        • XGBoost Regressor
      • Deep learning algorithms
      • Computer Vision
      • Natural Language Processing
      • Times Series processing
      • TensorFlow API
      • Helpers
      • Conclusion
  • CUSTOM MODULES
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  1. MODULE REFERENCEchevron-right
  2. CORE MODULES

Machine Learning algorithms

This section is about the Machine Learning algorithms.

ML modules in SmartPredictchevron-rightDecision Tree Regressorchevron-rightKNeighbors Classifierchevron-rightKNeighbors Regressorschevron-rightLinear Regressorchevron-rightLogistic Regressorchevron-rightMLP Regressorchevron-rightNaive Bayes Classifierchevron-rightRandom Forest Classifierchevron-rightRandom Forest Regressorchevron-rightSupport Vector Classifierchevron-rightSupport Vector Regressorchevron-rightXGBoost Classifierchevron-rightXGBoost Regressorchevron-right
PreviousEvaluator for ML modelschevron-leftNextML modules in SmartPredictchevron-right

Last updated 5 years ago

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