Page 210 - Ai_C10_Flipbook
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Step 13 Double-click the Image Viewer widget to view the images in the training dataset.
This step ensures that the images are correctly loaded and categorised into the two classes:
• Bleached • Unbleached
Step 14 Click the Close button to close the Image Viewer dialog box.
Step 15 Drag and drop the Image Embedding widget onto the canvas.
Step 16 Connect the Training Data to Image Embedding to extract image features and represent them as
embeddings (numerical feature vectors).
Step 17 Drag and drop the Data Table widget onto the canvas.
Step 18 Connect the Image Embedding widget to the Data Table.
Step 19 Double-click the Data Table widget to view the detailed feature representations of the images.
Each image is now represented by numerical vectors, which can be used for training Machine Learning
models.
Step 20 Click the Close button to close the Data Table dialog box.
Stage 4 Modelling
In this stage, a Machine Learning model is created using the processed dataset. The features extracted during Data
Exploration (from the Image Embedding widget) are used for training the model. The steps to build model are
as follows:
Step 21 Drag and drop the Test and Score widget onto the canvas.
Step 22 Connect the Image Embedding widget (or Data Table from the previous step) to the Test and Score
widget.
Step 23 Drag and drop widgets for the following classification algorithms:
• Logistic Regression • Random Forest • Support Vector Machine (SVM)
Step 24 Connect these algorithm widgets to the Test and Score widget.
Step 25 Double-click on the Test and Score widget to view the evaluation metrics for all three algorithms.
Key metrics to observe include:
• Accuracy: The proportion of correctly classified instances.
• F1 Score: The harmonic mean of Precision and Recall.
• Precision: The ratio of true positive predictions to the total predicted positives.
• Recall: The ratio of true positive predictions to the total actual positives.
Stage 5 Evaluation
In this step, the performance of different algorithms is evaluated to determine the best model for the task.
The steps for model evaluation are as follows:
Step 26 Drag and drop the Confusion Matrix widget onto the canvas.
Step 27 Connect the Test and Score widget (where you previously evaluated the algorithms) to the
Confusion Matrix widget.
This will allow you to visualize the confusion matrix based on the results from the tested algorithms.
208 Artificial Intelligence Play (Ver 1.0)-X

