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Top Interview Questions On Machine Learning
If you are preparing to clear Machine Learning interview then this blog post is the perfect guide to help you. This post consists of some of the most frequently asked questions on Machine Learning. Prepare yourself towards successfully facing your Machine Learning interview.
Top Interview Questions On Machine Learning-
- Is Rotation Necessary In PCA? If Yes, Why? What Will Happen If You Don’t Rotate The Components?
Rotation (orthogonal) is very much necessary as it helps in maximizing the difference between variance captured by the component. This rotation makes it easier for the components to interpret.
The prime motive behind PCA is to select fewer components which can explain the maximum variance in the data set. However this doesn’t mean that with rotation the relative location of the components gets changed, rather it only changes the actual coordinates of the points.
If rotation of components doesn’t occur then the effect of PCA will diminish and resulting in the increase in the number of components to explain variance in the data set.
- Why Is Naive Bayes So ‘Naive’?
Naive Bayes is so ‘naive’ since it assumes that all of the features in a data set are equally important and independent. Assumptions are rarely become true when it comes to the real-world scenarios
- Explain Reinforcement Machine Learning
Reinforcement Learning is an advanced concept that lets systems to understand depending on previous benefits for its activities. The system requires feedback for every activity that helps it in discovering whether its action is correct or incorrect. Reinforcement Machine Learning purely focused on the boosted effectiveness of the function.
- How Is True Positive Rate And Recall Related? Write The Equation.
True Positive Rate = Recall. Yes, they are equal having the formula (TP/TP + FN)
5. What Is The Difference Between Covariance & Correlation?
Correlation is the standardized form of covariance.
Its quite to compare Covariances. In the case where there exists two unequal scales it becomes almost impossible to compare. So as to deal with such situation, we calculate correlation to get a value between -1 and 1, irrespective of their respective scale.
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