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Step 39    Drag and drop the Predictions widget onto the canvas. This will allow you to make predictions based
                          on the trained model.





























               Step 40    Connect the Image Embedding widget (which processes the test data) to the Predictions widget.

                          This is the input data for making predictions.
               Step 41    Drag and drop the Logistic Regression widget to the
                          canvas (as this is the model you chose after evaluation).
               Step 42    Connect the Logistic Regression widget to the Predictions

                          widget. This will apply the Logistic Regression model to the
                          processed test data for classification.
               Step 43    Connect the Image Embedding widget (which contains
                          the  features  from  the  training  data)  to  the  Logistic
                          Regression  widget.  This  allows  the  Logistic  Regression
                          model to learn from the features extracted during training.
               Step 44    Double-click  on  the  Predictions  widget  to  view  the

                          output. The table will display the predictions for the test
                          data, including the predicted classes (Bleached or Unbleached) based on the Logistic Regression
                          model.
               Step 45    Click on the Close button to close the Predictions dialog box.



               Stage 6   Deployment
              Once the best-performing model is identified, the next step involves deploying the model to make predictions on
              new data or integrating it into a larger system.


                              Reboot


                     1.  How do No-code AI tools make AI development accessible to non-programmers?
                     2.  How does Orange help users with tasks like classification and regression without coding?



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