* During the process of optimization , the machine learning
algorithm searches the possible variants
of parameter
combinations in order to find
the best one which would allow the
correct mapping between the features and the classes during the process of training
* This process evaluates many potential candidate target
fuunctions from among those which a learning algorithm can guess
* The set of all the
potential functions that the learning algorithm can figure out is called
a Hypothesis Space
* One
can call the resulting classifier
with their set of parameters as a Hypothesis ,
which is a way in machine learning to say
that the algorithm has set parameters to replicate the target function and is
thus now ready to work out
correct classifications
* The hypothesis space space must
contain all the parameter variants of all the machine learning algorithms that
one may want to try to map to an
unknown function when solving a classification problem . This particular
sentence suggests that the entire sample
space takes into consideration , a
hypothesis space which would contain
all the possible variations in the
form of scenarios over where the
machine learning algorithm could manifest
itself at each point of time under
the conditions upto which a particular program has been evaluated till a particular point
of time and from which the Machine Learning algorithm would do a self analysis on its own for finding the best possible approach for a given condition or
problem . This is an instance example of
a condition to showcase
how a machine learning
algorithm would be doing a self
analysis for a possible condition and then take
the best possible course of action basing upon its own understanding and derived results .So
, elaborating more upon the aspect of hypothesis space .. one can deduce that
a hypothesis space generally consists of
a target function or a
similar approximation which is much different for
a similar function .
* The equivalent of this
could be thought of as the time
when a child in an effort to figure
out an image of a tree experiments with many different creative
ideas by assembling one's own knowledge
and experiences . Most certainly ,
parents play a major role in this learning phase and they provide all kinds of relevant environmental inputs for the faster and effective upbringing of the child . In Machine Learning , for say
in supervised learning algorithms one has
to
provide the right learning algorithms and with that one has to
provide some non-learnable parameters called
as hyper-parameters , next one has to choose
a set of examples to learn and adapt from
and then select the features that accompnay the examples . And just as a child cannot always learn to distinguish between right
and wrong if left alone in the world ( consider
the example of the case depicted in the book - Lord of the Flies ; summary is available at may sites where one can have
a quick synopsis of the story and save time
from reading the entire book which in these days is a very tedious , demanding and unproductive task ). In such a similar scenario as well , a
machine learning algorithm also needs multiple directions , multiple interjections
in order to facilitate the smooth
running and execution of a program .
*
So even
after the completion of the learning process , a machine
learning classifier often cannot unequivocally map the
examples to the target classification because many false and
erroneous mappings are possible which
could mar the generation of best
possible results and then render the learning process ineffective as the learning
algorithm in its path to effective learning picks up erroneous and wrong paths and
lands up adding insufficient data points to discover
the right function . In addition to this , conditions of noise
( this aspect is also a great factor in machine learning ) also affect
the process of learning
* In real world as well , Noise plays a same kind of impediment factor in the process of
learning which derides the effective learning mechanism . Similarly , many such extraneous factors and errors also occur which during the process of recording
of the data which distort the values
and features to be read and
understood . In true sense ,
therefore it is considered that a good machine
learning algorithm should distinguish the signals that can map back to
a target function even though extraneus environmental noise is still in play .
Last
modified: 27 Apr 2021
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