SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
      • Key features
      • Who may benefit from its use
      • The SmartPredict Graphical User Interface (GUI)
        • The SmartPredict Modules
        • Focus on the Notebooks
        • Practical shortcuts
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
      • Project description
      • Step 1. Create the project
      • Step 2. Upload the dataset
      • Step 3. Preprocess the dataset
      • Step 4. Build the flowchart
        • Set up the flowchart
        • Configure the modules
      • Step 5. Run the build
      • Step 6. Deploy the project
      • Step 7. Make inferences with our model
      • Conclusion
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
    • Project description
    • Step 1. Create the project
    • Step 2. Upload the dataset
    • Step 3. Preprocess the dataset
    • Step 4. Build the flowchart
    • Step 5. Run the build
    • Step 6. Deploy the pipeline
    • Step 7. Make inferences with our pipeline
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
        • Item Saver
      • Web Services
        • Web Service IN and OUT
      • Data retrieval
        • Data fetcher
        • Data frame loader/converter
        • Image data loader
      • Data preprocessing
        • Introduction
        • Array Reshaper
        • Generic Data Preprocessor
        • Missing data handler
        • Normalizer
        • One Hot Encoder
        • Ordinal Encoder
      • Data selection
        • Features selector
        • Generic data splitter
        • Labeled data splitter
      • Training and Prediction
        • Predictor DL models
        • Predictor ML models
        • Predictor ML models (Probabilistic models)
        • Trainer ML models
        • Trainer/Evaluator DL models
      • Evaluation and fine-tuning
        • Cross Validator for ML
        • Evaluator for ML models
      • 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
        • Dense Neural Network
        • Recurrent Neural Networks
      • Computer Vision
        • Convolutional Recurrent Networks
        • Fully Convolutional Neural Networks
        • Face detector
        • Image IO
        • Image matcher
        • Yolo
      • Natural Language Processing
        • Introduction
        • Text cleaner
        • Text vectorizer
      • Times Series processing
        • TS features selector
      • TensorFlow API
        • LSTM Layer
        • Dense Layer
      • Helpers
        • Data/Object Logger
        • Object Selector (5 ports)
      • Conclusion
  • CUSTOM MODULES
    • Function
    • Class
    • Use cases
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  1. MODULE REFERENCE

CORE MODULES

In this new section we are going to see the different modules included in the SmartPredict palette of core modules , their roles and how to configure them.

PreviousStep 7. Make inferences with our pipelineNextIntroduction

Last updated 5 years ago

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SmartPredict Module Reference

    1. Item Saver

    1. Web Service IN

    2. Web Service OUT

    1. Data Fetcher

    2. Data Frame loader/converter

    3. Image Data Loader

    1. Array Reshaper

    2. Generic Data Preprocessor

    3. Missing data handler

    4. Normalizer

    5. One Hot Encoder

    6. Ordinal Encoder

    1. Features selector

    2. Generic data splitter

    3. Labeled data splitter

    1. Predictor DL models

    2. Predictor ML models (Probabilistic models)

    3. Trainer ML models

    4. Trainer/Evaluator DL models

    1. Cross Validator for ML

    2. Evaluator for ML models

    1. KNeighbors Classifiers

    2. KNeighbors Regressor

    3. Linear Regressor

    4. Logistic Regressor

    5. MLP Classifier

    6. Random Forest Classifier

    7. Random Forest Regressor

    8. Support Vector Classifier

    9. XGBoost Classifier

    10. XGBoost Regressor

    1. Dense Neural Networks

    2. Recurrent Neural Networks

    1. Convolutional Recurrent Networks

    2. Face detector

    3. Fully Convolutional Neural Networks

    4. Image IO

    5. Image matcher

    6. Yolo

    1. Text Cleaner

    2. Text Vectorizer

    1. TS Features selector

    1. Dense Layer

    2. LSTM Layer

    1. Data/Object Logger

    2. Object Selector (5ports)

Basic Operations
Web Service
Data Retrieval
Data Preprocessing
Data Selection
Training and Prediction
Evaluation and Fine Tuning
Machine Learning Algorithms
Deep Learning Algorithms
Computer Vision
Natural Language Processing
Time Series Processing
TensorFlow2 API
Helpers