In the NumPy function, the data preparation technique that is used to help machine learning algorithms is called the reshape technique or function
For better understanding, let us explain what the reshape function means
- The numpy package helps to give the right tools for scientific and mathematical computations in python
. it includes functions that cam be used to perform common linear algebra operations, fast Fourier transforms, and statistics
The reshape function simply alter or change the row and column arrangement of data in numpy function and it is said to just give new shape to an array without the altering of its data.
from the above, we can therefore say that the answer In the NumPy function, the data preparation technique that is used to help machine learning algorithms is called the reshape technique or function is correct
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Answer:
log base 3a= -0.631.log a/3 base 3
Now, -log m= log 1/m
hence,
log base 3a= 0.631.log 3/a base 3
log base 3a/log 3/a base 3 =0.631
log base 3 ( a.3/a) =.631 since, log m/logn =log n(m)
log base 3 3=0.631
Hence, answer is log base 3 3=0.631
Explanation:
Please check the answer section.
"detection" by checking for possible cycles or knots.
Answer:
C. Offset.
Explanation:
An offset operator can be defined as an integer that typically illustrates or represents the distance in bytes, ranging from the beginning of an object to the given point (segment) of the same object within the same data structure or array. Also, the distance in an offset operator is only valid when all the elements present in the object are having the same size, which is mainly measured in bytes.
Hence, the offset operator returns the distance in bytes, of a label from the beginning of its enclosing segment, added to the segment register.
For instance, assuming the object Z is an array of characters or data structure containing the following elements "efghij" the fifth element containing the character "i" is said to have an offset of four (4) from the beginning (start) of Z.
Answer:
Explanation:
The minimum depth occurs for the path that always takes the smaller portion of the
split, i.e., the nodes that takes α proportion of work from the parent node. The first
node in the path(after the root) gets α proportion of the work(the size of data
processed by this node is αn), the second one get (2)
so on. The recursion bottoms
out when the size of data becomes 1. Assume the recursion ends at level h, we have
(ℎ) = 1
h = log 1/ = lg(1/)/ lg = − lg / lg
Maximum depth m is similar with minimum depth
(1 − )() = 1
m = log1− 1/ = lg(1/)/ lg(1 − ) = − lg / lg(1 − )
