In 8 steps: 1) data selection, 2) data configuration, 3) model building, 4) model training, 5) morphological space creation, 6) machine learning-based morphological space analysis, 7) generation of morphological model, 8) morphological model solutions review.
After completing a step, the next step will appear until you reach the last step.
Step 1: Select CSV Data
Upload a CSV file where each row represents a record and each column represents a variable. The computation will occur on your device using TensorFlow.js, so be mindful of the data size.
Step 2: Configure Data
Check and correct the type of the data in columns. Then select the output (y) variable to fit model to; this variable will be used as a reference for the morphological model. Only binary or numerical types are allowed as output.
Input data
Output data
Step 3: Build Model
Add hidden layers to the model. The builder will automatically add the last (output) layer based on the output variable type you selected. Ensure your model is appropriately complex to capture the relationships in your data.
Step 4: Model Training
Set the training hyperparameters and start the training process. The number of epochs determines how many times the model will iterate over the entire dataset. Batch size is the number of samples per gradient update. The train-validate split ratio determines the proportion of data used for training versus validation. The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated.
Step 5: Generate Synthetic Data
Generate synthetic data to create the morphological space. Numeric variables will be divided into intervals, and binary/categorical variables will use their unique values. Specify the numeric resolution to control the interval size for numeric variables.
Step 6: Apply Model to Synthetic Data
Apply the trained model to predict outcomes for the synthetic data (morphological space). This step will generate predictions that will be used in the morphological model.
Step 7: Generate Morphological Model
Create the morphological model based on the machine learning predictions. This model will help analyze and visualize the relationships and dependencies within the morphological space.
Step 8: View Morphological Model
View the generated morphological model. You can download the report file for future use without needing to retrain the model or regenerate it. The cell colors reflect the maximum (or minimum, depending on the mode selected, the lighter the worse) possible scores, and dots indicate the highest score combinations.