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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On this page
  • Configuring the flowchart
  • 1. Dealing with the features Selector
  • 3. Splitting training and testing data with the labeled data splitter

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  1. Getting started
  2. Getting started (Part1) : Iris classification project
  3. Step 4. Build the flowchart

Configure the modules

Module configuration includes some preliminary module settings as well as their roles in the model.

PreviousSet up the flowchartNextStep 5. Run the build

Last updated 5 years ago

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Configuring the flowchart

Before starting to configure our flowchart, we still need to initiate some modifications.

1. Dealing with the features Selector

Having dismissed the data loader , let us deal with the features selector before splitting our dataset. Afterwards, we can finally start running our project.

As mentioned earlier, the iris flowers are classified according to the following distinctive relevant features:

  • petal width,

  • petal length,

  • sepal width,

  • sepal length.

Those are exactly the information we shall need to insert in our features selector.

Click on ‘Parameters’ then ‘Get columns from the dataset’. Under ‘Get columns’, the ‘selected columns’ instantly unlocks to reveal a scroll down menu of the iris classification features.

We need to include all features by adding them one by one.

To do so, click successively on every feature and follow up until the last one in the list. Click on the button ‘Add ’ or press the Enter key to validate each entry .

For selected label , however, as an output we shall need but ‘variety ’ since it is the only result we would like to be delivered. Also, enter ‘variety’ in the selected label category.

Once it is done, click on ‘Save’.

3. Splitting training and testing data with the labeled data splitter

The role of the labeled data splitter , as its name suggests, is to divide datasets into training and testing sets. Upon clicking on the module, a slider enables to variate this ratio at will.

N.B For machine learning purposes, the train proportion is usually greater than the test with an optimal value between 0.5-0.8

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The features selector as its name suggests is for choosing which features to use.
The labeled data splitter sort data into training and testing sets.