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S = <phi, phi, phi>Training data = <rainy, cold, white> => No (negative example). How will S be represented after encountering this training data?

Correct Answer: <phi, phi, phi>
When a negative example is encountered, the Find-S algorithm ignores it. Hence, the hypothesis remains unchanged. It will only a change, when a positive example inconsistent with data is encountered.

S = Training data = => No (negative example). How will S be represented after encountering this training data?

S = Training data = => Yes (positive example). How will S be represented after encountering this training data?

S = . Training data = => Yes (positive example). How will S be represented after encountering this training data?

G = . Training data = => Yes (positive example). How will G be represented after encountering this training data?

G = ( ; ; ). Training data = => Yes (positive example). How will G be represented after encountering this training data?

The value of $$\left( {\frac{{\sin \theta + \sin \phi }}{{\cos \theta + \cos \phi }} + \frac{{\cos \theta - \cos \phi }}{{\sin\theta - \sin\phi }}} \right)$$       is?

The cumulative distribution function for the lognormal distribution is given by _____ \) b) \(\phi\) c) \(\phi\) d) \(\phi\)

If the electrostatic potential were Igiven by $$\phi = {\phi _0}$$  (x2 + y2 + z2), where $${\phi _0}$$ is constant, then the charge density giving rise to the above potential would be

We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?

Which is the part of training information system?
1. Opportunities
2. Training executed
3. Post training developments
4. Skill gaps
5. Number redeployed after training
6. Number retained in other new trades/skill