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. Getting started
  2. Getting started (Part1) : Iris classification project

Step 4. Build the flowchart

In this section we are going to build our iris classification project flowchart.

PreviousStep 3. Preprocess the datasetNextSet up the flowchart

Last updated 5 years ago

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The build workspace shows a detailed view of the current build workflow. Since we have chosen a classification project, the preset workflow for such projects type displays. It is only the default configuration applied for similar projects . However, we can always modify it later.

The default classification project Workflow contains the following modules:

  • Dataframe loader/converter

  • Features selector

  • Support Vector Classifier (Our ML model)

  • Labeled data splitter

  • Trainer ML models

  • Evaluator

  • Item saver

For building our iris classification project :

  1. we need to configure those modules and

  2. furthermore add :

We are going to develop these steps in in the next sections.

To adjust the viewport, below the menu, there is a set of functions to choose from:

  • zooming/magnifying

  • reducing the environment size

  • seizing and moving the whole flowchart...

The build workspace is where we design our first workflow. The viewport can be fitted at will.