Question No 1

Feature Hashing approach is "SGD - based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size" now with large vectors or with multiple locations per feature in Feature hashing?

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Is a problem with accuracy

It is hard to understand what classifier is doing

It is easy to understand what classifier is doing

Is a problem with accuracy as well as hard to understand what classifier us doing

Question No 2

What are the advantages of the Hashing Features?

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Requires the less memory

Less pass through the training data

Easily reverse engineer vectors to determine which original feature mapped to a vector location Answer: AB Explanation: SGD - based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, textlike, or word - like data. This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing. An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word - like variables aren't a problem.

Question No 3

Question - 3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space - efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?

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Question No 4

Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,

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Question No 5

What is the considerable difference between L1 and L2 regularization?

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L1 regularization has more accuracy of the resulting model

Size of the model can be much smaller in L1 regularization than that produced by L2 - regularization

L2 - regularization can be of vital importance when the application is deployed in resource - tight environments such as cell - phones.

All of the above are correct

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