AI Steps

Which step involves gathering data from various sources?

• Model Training

• Feature Engineering

• Data Collection

• Model Deployment

What must be done to data to ensure consistency?

• Normalization or standardization

• Feature selection

• Hyperparameter tuning

• Model integration

Which of the following is NOT considered during model selection?

• Problem type

• Accuracy

• Data collection

• Computational efficiency

What should be used to evaluate the model’s performance during training?

• Test set

• Training set

• Validation set

• Deployment setup

Which step involves optimizing the model's parameters to minimize error?

• Data Preprocessing

• Model Evaluation

• Model Training

• Feature Engineering

In which step should the model be retrained periodically with new data?

• Model Deployment

• Data Collection

• Monitoring and Maintenance

• Model Selection

What can be performed to find the best set of parameters for the model?

• Data normalization

• Feature extraction

• Hyperparameter tuning

• Model deployment

What must be continuously monitored in the real world?

• Data preprocessing

• Model’s performance

• Feature engineering

• Algorithm selection

Which step involves integrating the trained model into a production environment?

• Data Preprocessing

• Model Selection

• Model Training

• Model Deployment

What should be considered when selecting an algorithm or model?

• The type of data split

• Hyperparameter tuning

• Factors like accuracy and interpretability

• The feedback loop