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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  • Description
  • The input ports
  • The output port
  • Parameters

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  1. MODULE REFERENCE
  2. CORE MODULES
  3. Evaluation and fine-tuning

Cross Validator for ML

This module belongs to the ""Evaluation and fine-tuning" category of modules.

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Last updated 5 years ago

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Description

The Cross Validator is used for evaluating a Machine Learning model using the cross validation method. It outputs the relative scores of the model according to your scoring method. You can get the mean, and the standard deviation of the scores with the mean() and std() method of the output.

This module has three input ports and one output port.

The input ports

  1. The first input port takes the model to evaluate .

  2. The second input port takes the features used for evaluating the model .

  3. The third port takes the labels for evaluating the model.

The output port

Parameters

Scoring methods can be set by selecting one from the dropdown :

  • Balanced accuracy (Classification) , Average precision, Brier score loss, F1 score , F1 Micro, F1 Weighted , F1 Samples ,Cross entropy Precision, Macro -precision ,Weighted-precision, Sample precision, Recall (Binary Classification ), Macro-recall ,Micro-recall ,Weighted-recall, Samples recall (Multi -label classification ), Adjusted mutual information score (Clustering), Adjusted Rand Index, Completeness score, Fowlkes Mallows score ,Homogeneity Score, Mutual Information score, Normalized information score ,V-measure Cluster (Clustering) ,Explained variance (Regression), Negative mean squared error (Regression), Negative mean squared logarithmic error (Regression) , Negative median absolute (Regression), R2 score (Regression) and more.....

Equally, we can choose what to output. This might be : Scores, Mean, Standard deviation and Median.

N.B : K is for k-fold CV

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The three inputs of the Cross Validator have all to be connected.
As an output, this port gets back the selected element from the parameters.
We can choose how to score or model among the enriched list of methods.