Behaviours

Has Experience With Model Evaluation And Validation

Skilled in evaluating AI models’ performance using metrics and validation techniques, such as cross-validation and confusion matr...

Behaviours

Has Experience With Model Evaluation And Validation

What key metrics do you primarily focus on when evaluating the performance of an AI model, and why?

Behaviours CARD

Has Experience With Model Evaluation And Validation

Skilled in evaluating AI models’ performance using metrics and validation techniques, such as cross-validation and confusion matrices.

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BEHAVIOURS CARD

The card itself

Pages 199 and 200 of the Better Deck.

Front

Behaviours

Has Experience With Model Evaluation And Validation

Skilled in evaluating AI models’ performance using metrics and validation techniques, such as cross-validation and confusion matr...

Behaviours

Has Experience With Model Evaluation And Validation

What key metrics do you primarily focus on when evaluating the performance of an AI model, and why?

Back

Behaviours

Has Experience With Model Evaluation And Validation

Skilled in evaluating AI models’ performance using metrics and validation techniques, such as cross-validation and confusion matr...

Behaviours

Has Experience With Model Evaluation And Validation

What key metrics do you primarily focus on when evaluating the performance of an AI model, and why?

FRONT OF CARD

Front text

Extracted from page 199.

1. Proficiency in selecting appropriate evaluation metrics, such as accuracy, precision, recall, or F1 score, to assess model performance.

2. Understanding of validation techniques, including train-test splits, crossvalidation, and stratified sampling, to ensure unbiased model assessment.

3. Experience in fine-tuning model hyperparameters to optimise model performance and generalisation.

4. Knowledge of techniques for detecting and addressing common issues in model evaluation, such as overfitting, underfitting, or data leakage.

5. Proficiency in using validation curves, learning curves, or ROC curves to gain insights into model behaviour and performance.

6. Knowledge of ensemble methods and model averaging techniques for improved model evaluation and performance.

IMPACT H A S E X P E R I E N C E W I T H M O D E L E V A L U A T I O N A N D V A L I D A T I O N

Better The Deck

CRAFT (MIMTSK) CHARACTER (WIRE) CAPACITY (ME) DRIVE (TV) =

BACK OF CARD

Back text

Extracted from page 200.

1. What key metrics do you primarily focus on when evaluating the performance of an AI model, and why?

2. Can you describe a specific instance where you effectively used cross-validation to enhance an AI model’s accuracy?

3. What strategies or techniques do you commonly use to fine-tune AI models and optimize their performance?

4. How do you approach adjusting parameters within AI models to minimize errors and improve results?

5. Can you share an experience where your evaluation and adjustment of an AI model significantly improved its performance in a real-world application?

COMPETENCIES FOR WORKING WITH ARTIFICIAL INTELLIGENCE – MACHINE LEARNING H A S E X P E R I E N C E W I T H M O D E L E V A L U A T I O N A N D V A L I D A T I O N

Better The Deck

REFER TO COACHING ROUND THREE INSTRUCTIONS

FRAMEWORK CONTEXT

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Round 3 - Connect to Competencies Translate capability needs into observable behaviours that can be sorted and coached.

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Has Experience With Model Evaluation And Validation

Has Experience With Model Evaluation And Validation

Has Experience With Model Evaluation And Validation

The card itself

Has Experience With Model Evaluation And Validation

Has Experience With Model Evaluation And Validation

Has Experience With Model Evaluation And Validation

Has Experience With Model Evaluation And Validation

Front text

Back text

Where this card fits

Coaching rounds

Recipes

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