RE: LeoThread 2024-08-31 09:20

[taskmaster4450le]

1. Model selection:

   • Choose an appropriate algorithm (e.g., linear regression, decision trees, neural networks)
   • Consider factors like data type, size, and complexity of the problem

2. Training process:

   • Split data into training and testing sets
   • Use training data to teach the model patterns and relationships

3. Evaluation metrics:

   • Define how you'll measure the model's performance (e.g., accuracy, precision, recall)