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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?
210 Artificial Intelligence Play (Ver 1.0)-X

