a Mrs. X has lost gender information of one of her customers, and does not know...

 

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a Mrs. X has lost gender information of one of her customers, and does not know whether to make a skirt or trousers. The customer who is missing gender information has only the measurement of waist and hip which are 28 and 34 respectively. Using K-NN classifier with K=3, find the missing gender information. The training set is given in Table 3. Table 3: Training set S/N Waist (cm) Hip (cm) 32 35 Gender Male 28 33 27 31 Male Female Female ? 3 33 4 36 28 34 Imagine you are dealing with text data. To represent the words you are using word embedding, i.e. representing words as vector of tokens. In word embedding, you will end up with 1000 dimensions. Now, you want to reduce the dimensionality of this high dimensional data such that, similar words should have a similar meaning. In such case, which algorithm are you most likely choose? Explain mathematically how you are going to reduce dimensions. a Mrs. X has lost gender information of one of her customers, and does not know whether to make a skirt or trousers. The customer who is missing gender information has only the measurement of waist and hip which are 28 and 34 respectively. Using K-NN classifier with K=3, find the missing gender information. The training set is given in Table 3. Table 3: Training set S/N Waist (cm) Hip (cm) 32 35 Gender Male 28 33 27 31 Male Female Female ? 3 33 4 36 28 34 Imagine you are dealing with text data. To represent the words you are using word embedding, i.e. representing words as vector of tokens. In word embedding, you will end up with 1000 dimensions. Now, you want to reduce the dimensionality of this high dimensional data such that, similar words should have a similar meaning. In such case, which algorithm are you most likely choose? Explain mathematically how you are going to reduce dimensions.

Answer rating: 100% (QA)

Winist Com Hiecim Gender Dtance Rank 28 32 Male 2 2 33 35 Male 5 019 3 F... View the full answer