Skip to main content

Classification Model Predictor

Description#

The predictor creates a model based on the data distribution. Then the model is used to predict future or unknown values.

Properties#

Input#

  • Algorithm Type – It specifies the classification algorithm for prediction. Select from Random Forest, Support Vector Machine, Decision Tree.
    • Random forest – A meta estimator tries to fit several decision trees on various sub-samples of datasets and uses predictive accuracy of the model and controls over-fitting.
    • Support Vector Machine – It represents the training data as points in space divided into categories by a clear gap. Then, it maps the new examples into the same space and predicts a category based on which side of the gap they fall.
    • Decision Tree – It produces the sequences of rules used to classify the data. It is simple to understand, requires less data preparation, and can handle numerical and categorical data.
  • Input Data – Data for predicting values.
  • Model Name – Generated model name for prediction.

Misc#

  • DisplayName – Add a display name to your activity.
  • Private – By default, activity will log the values of your properties inside your workflow. If private is selected, then it stops logging.

Output#

  • Result – Prediction value returned by specified model.

Example#