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S = <phi, phi, phi> Training data = <square, pointy, white> => Yes (positive example). How will S be represented after encountering this training data?

Correct Answer: <square, pointy, white >
Initially, S contains phi, which implies that no example is positive. It encounters a positive example, which is inconsistent with the current hypothesis. So, it generalizes accordingly to approve the new example. It thus takes the values of the training instance.

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

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?

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) \(\phi\)

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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?