Page 210 - Ai_C10_Flipbook
P. 210

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
   205   206   207   208   209   210   211   212   213   214   215