Hello and Welcome from 360DigiTMG. This is a video playlist to explain the life cycle of a data science project. In the previous video we talked about Model Development. Now let us discuss about the next phase in the data science lifecycle – Model Evaluation.

Model Evaluation: This is the penultimate step of the data science life cycle. After the model has been trained, evaluate it to determine if its performance and accuracy will enable you to achieve your business goals. You might want to generate multiple models using different methods and evaluate the effectiveness of each model. For example, you could apply different business rules for each model, and then apply various measures to determine each model's suitability. You also might evaluate whether your model needs to be more sensitive than specific, or more specific than sensitive. For multiclass models, evaluate error rates for each class separately.
You can evaluate your model using historical data (offline evaluation) or live data (online evaluation). In offline evaluation, the trained model is evaluated with a portion of the dataset that has been set aside as a holdout set. This holdout data is never used for model training or validation—it’s only used to evaluate errors in the final model. The holdout data annotations need to have high accuracy for the evaluation to make sense. Allocate additional resources to verify the accuracy of the holdout data.

Best practices for evaluating a model:

• Have a clear understanding of how you measure success
• Evaluate the model metrics against the business expectations for the project
• Plan and execute Production Deployment (Model Deployment and Model
Inference)

That concludes this video. In the final video we will talk about the next phase in the life cycle – Model Deployment and Monitoring.


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