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Could you define the concepts of overfitting and underfitting in machine learning, and explain their relevance in model development?

Machine Learning Engineer

Apple

Microsoft

Amazon

Palantir Technologies

Cohesity

Cleo

Could you define the concepts of overfitting and underfitting in machine learning, and explain their relevance in model development?
What are overfitting and underfitting in machine learning? Why are they crucial factors to consider when developing models?
In the field of machine learning, what do we mean by overfitting and underfitting? Why is it necessary to keep these phenomena in mind during model development?
How do you explain overfitting and underfitting in machine learning, and what is their significance in the development of models?
Can you describe the concepts of overfitting and underfitting in machine learning? Why is it important to address these concerns when building models?
What exactly are overfitting and underfitting in machine learning, and why do they matter in the process of model development?
Explain the notions of overfitting and underfitting in machine learning, and discuss why they are crucial considerations in the building of models.
To someone unfamiliar with machine learning, how would you explain overfitting and underfitting? How do these concepts shape the development of models?
Why are overfitting and underfitting important concepts to understand when working with machine learning models? What are they, exactly?
When it comes to machine learning, what do we mean by overfitting and underfitting, and why are they factors to bear in mind throughout model development?
Define overfitting and underfitting in machine learning, and explain why they are important considerations in model development.

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Interview question asked to Machine Learning Engineers interviewing at Peek, Apple, Microsoft and others: Could you define the concepts of overfitting and underfitting in machine learning, and explain their relevance in model development?.

Could you define the concepts of overfitting and underfitting in machine learning, and explain their relevance in model development?

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Could you define the concepts of overfitting and underfitting in machine learning, and explain their relevance in model development?

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