Answer / Bobindra

Ensemble learning is a Machine Learning technique that combines multiple models to improve the predictive performance and reduce overfitting. In ensemble learning, multiple weak learners are combined to produce a strong learner or a single powerful model. The key idea behind ensemble learning is to use diversity in individual models to improve the overall accuracy by averaging out their errors.

Do you think that treating a categorical variable as a continuous variable would result in a better predictive model?

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What do you mean by ensemble learning?

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What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?

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Tell me how a roc curve works?

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What is data structure? And what are the different types of data structures supported in r programming?

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what is the function of ‘unsupervised learning’?

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Mention any one of the data visualization tools that you are familiar with?

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How do you think google is training data for self-driving cars?

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Explain the decision tree classification?

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What is type I vs type ii error?

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Which language is best for deep learning?

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What is stratified cross-validation and when should we use it?

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