Train a Machine Learning Model

Train a machine learning model based on labeling information it contains.

1. From the Assembly ribbon, click the Classify > Train tool.

   
   
   Figure 1.

2. From the guide bar, select the Classifier drop-down menu to select an existing classifier to train.
3. Click Train.
   After training a machine learning model, a binary file (<classifier_name>_<train_iteration>.aic) containing all training information is saved in the Classifier folder. If you add new labels to a classifier or you modify existing label assignments, retrain the machine learning model to apply the changes.

   Note: The value of <train_iteration> increments by one after every successful training operation. The number of train iterations starts at 0 and the <train_iteration> suffix is appended to the file names only when train_iteration > 0.

   You can use the resultant binary (.aic) files from model training on different machines. As a result, an advanced user can create the classifiers and train the machine learning models to provide files for other users to perform part classification, eliminating the need to train a classifier (See Add Trained Model File).

   Tip: From the guide bar, click and select Generate Log File, if necessary. The log files (<classifier_name>_<train_iteration>_log.txt) are saved to the Classifier folder after training a machine learning model.

After the classifier has been trained, the confusion matrix is displayed. The confusion matrix displays the accuracy of the trained machine learning model versus the labels that you provided. The confusion matrix is displayed by default. Click to toggle the matrix on/off.

Figure 2. Confusion Matrix

Each row shows the label name and the total number of parts given that label by the user. This sum is displayed under Support.

Each column shows the label predicted by the machine learning model.

Precision

The ratio of the number of correctly predicted labels of a given type to the total number of predictions of that same label. Provides insight into the ability of the machine learning model to predict the label correctly.

Support

The total number of parts with each label (user defined).

Overall Accuracy

The total ratio of correct predictions to the total number of predictions.