Answer / Nanak Singh

The measure that determines the distance between the predicted and actual values in machine learning is called Error or Loss Function. Examples include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).

How do you choose an algorithm for a classification problem?

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What is Inductive Logic Programming in Machine Learning?

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If a highly positively skewed variable has missing values and we replace them with mean, do we underestimate or overestimate the values?

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Is naive bayes supervised learning?

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What is statistical reasoning in artificial intelligence?

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What is motor sequence learning?

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Can you name some feature extraction techniques used for dimensionality reduction?

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Can you explain rescaling data technique in data pre-processing?

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Differentiate between inductive and deductive machine learning?

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How do you control for biases?

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What was bayes’ theorem? How was it useful in a machine learning context?

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Give examples of machine-to-machine communications?

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