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What is the most frequent metric to assess model accuracy for classification problems?
How do we know which machine learning algorithm is better for us to solve our problem?
How will you choose the most appropriate machine learning algorithm for your classification problem?
When should one use mean absolute error over root mean square error as a performance measure for regression problems?
What does linear in ‘linear regression’ actually mean?
What is the “curse of dimensionality?
What do you understand by decision tree classification?
What do you mean by parametric models?
How do we separate one dimensional, two dimensional and three-dimensional data?
How will you explain machine learning into a layperson?
What do you mean by parametric models? Also, give some examples of them?
Why do we convert categorical variables into factor?
What type of learning is needed when the system needs to adapt to rapidly changing data?
How to decide one problem is a machine learning problem or not?
What is the sigmoid function in machine learning?
