STEP 1 - CHOOSE DATASET
Choose the dataset from the preset dataset given. These dataset represent different AI problems including binary classification and regression
STEP 2 - INSPECT DATASET
Click View Dataset to see the details. It is to understand the dataset better
STEP 3 - VIEW MODEL ARCHITECTURE
The blue boxes indicate the hyperparameters involved for tuning
STEP 4 - SPECIFY MIN AND MAX VALUE FOR THE HYPERPARAMETER USED
Recommended to key-in the number of nodes and learning rate as specified in the example
STEP 5 - SPECIFY THE NUMBER OF TRIAL
Key-in the number of trials per round in between 5-10
STEP 6 - RUN HYPERPARAMETER OPTIMIZATION
It breaks the search space into small sections where less trials are needed to achieve high accuracy
STEP 7 - OPTIMIZATION RESULTS
The results include the maximum validation accuracy/ minimum validation mae, search space boundaries, validation accuracy/ validation mae distribution
