Q10. You are working on a time series data set. Your manager has asked you to build a high accuracy model. You start with the decision tree algorithm since you know it works fairly well on all kinds of data. Later, you tried a time series regression model and got higher accuracy than the decision tree model. Can this happen? Why?

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  • Time series data is based on linearity while a decision tree algorithm is known to work best to detect non-linear interactions
    Decision tree fails to provide robust predictions. Why?
  • The reason is that it couldn’t map the linear relationship as good as a regression model did.
  • We also know that a linear regression model can provide a robust prediction only if the data set satisfies its linearity assumptions.