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NEW QUESTION 48
You are working on a email spam filtering assignment, while working on this you find there is new word e.g.
HadoopExam comes in email, and in your solutions you never come across this word before, hence probability
of this words is coming in either email could be zero. So which of the following algorithm can help you to
avoid zero probability?
Answer: C
Explanation:
Explanation
Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more
robust and will not fail completely when data that has never been observed in training shows up.
NEW QUESTION 49
Which of the following commands will return records from an existing Delta table my_table where duplicates
have been removed?
2. FROM my_table;B. 1. SELECT DISTINCT *
2. FROM my_table;C. 1. MERGE INTO my_table a
2. USING new_records b;D. 1. MERGE INTO my_table a
2. USING new_records b ON a.id = b.id
3. WHEN NOT MATCHED
4. THEN INSERT *;E. 1. SELECT *
2. FROM my_table
3. WHERE duplicate = False;
Answer: B
NEW QUESTION 50
Projecting a multi-dimensional dataset onto which vector has the greatest variance?
Answer: E
Explanation:
Explanation
The method based on principal component analysis (PCA) evaluates the features according to the projection of
the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear
discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant
vector.
The first principal component corresponds to the greatest variance in the data, by definition. If we project the
data onto the first principal component line, the data is more spread out (higher variance) than if projected onto
any other line, including other principal components.
NEW QUESTION 51
......
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