SmartPredict
Ctrlk
  • 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 REFERENCE
  2. CORE MODULES

Machine Learning algorithms

This section is about the Machine Learning algorithms.

ML modules in SmartPredictDecision Tree RegressorKNeighbors ClassifierKNeighbors RegressorsLinear RegressorLogistic RegressorMLP RegressorNaive Bayes ClassifierRandom Forest ClassifierRandom Forest RegressorSupport Vector ClassifierSupport Vector RegressorXGBoost ClassifierXGBoost Regressor
PreviousEvaluator for ML modelsNextML modules in SmartPredict

Last updated 5 years ago

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