Exploring Cost Functions in ML

 

*  The driving force behind the concept of optimization in machine learning is the response from a function which is internal to the algorithm which is called as a Cost Function

 *  One may see other terms used in some contexts , such as loss function , objective function , scoring function , or error function but the cost function is an evaluation function that measures how well the machine learning algorithm maps the target function that the function was striving to guess

 

*  In addition , a cost function determines how well a machine learning algorithm performs in a supervised prediction or an unsupervisd optimisation problem

  

*  The Evaluation function works by comparing the algorithm predictions against the actual outcome recorded from the real world .

 

*     Comparing a prediction against a real value using a cost function which determines the algorithm's error level

 

*     Since it is a mathematical formulation , a general cost function expresses the error level in a numerical form thereby keeping the errors low . This means that the cost function modulates according to the parameters of a function in order to tune the produced output to a more numeric form thereby keeping the errors of the overall output to a low .

  

*  The cost function transmits whatever is actually important and meaningful for the purposes of the learning algorithm

  

*  As a result , when considering a scenario like stock market forecasting , the cost function expresses the importance of avoiding incorrect predictions . In such a case , one may want to make some money by avoiding any sort of big losses . In forecasting sales , the concern is different because one needs to reduce the error in common and frequent situations and not in the rare and exceptional cases , as one uses a different cost function .

*     Example -- While considering stock market forecasting , the cost function expresses the importance of avoiding incorrect predictions . In such a case , one may want to make some money by avoiding big losses

  

*  When the problem is to predict who would likely become ill from a certain disease , then for this also algorithms are in place that can score a higher probability of singling out the people who have the same characteristics and actually did become ill at a later point of time . And based on the severity of the illness , one may also prefer that the algorithm wrongly chooses some people who do not get ill , rather misses out on the people who actually get ill

  

*  So after going through the given aspects on the usability of cost functions  and how they are coagulated with some ML algorithms in order to fine tune the result .we will get to see and check the method of Optimisation of a Cost Function and how and why they are done

 

*   Optimisation of Cost Functions :

It is widely accepted as a conjecture that the cost function associated with a Machine Learning generic function is what truly drives the success of a machine learning application . This is an important part of the process of representation of an associated cost function that is the capabilty of an algorithm to approximate certain mathematical functions and along with that do some necessary optimisation which means how the machine learning algorithm sets their internal parameters .

 

*  Most of the machine learning algorithms have their own optimisation which is associated with their own cost functions which means some of the better developed and advanced algorithms of the time are well capable enough to fine tune their algorithms on their own and can come at a best optimised result at each step of the formulation of machine learning algorithms . This leaves the   role of the user as futile some of the times as the role of the user to fine tune the learning process and preside over the aspects of learning are not so relevant .

 

*  Along with such , there are some algorithms that allow you to choose among a certain number of possible functions which provide more flexibility to choose their own course path of learning

 

*  When an algorithm  uses a cost  function directly in the  optimisation process , the cost function is used internally . As the algorithms are set to work with certain cost functions , the objective of the optimisation problem may differ from the desired objective .

 

*  And as the algorithms set to work with some of the cost functions , the optimisation objectives may also differ from the desired objective . In such a circumstance where the associated cost function could be used , one can call an error function or a loss function .. an error function is where the value needs to be minimised ; and the reverse of it is called a scoring function if the objective for   the function is to maximise the result .

  

*  With respect to one's target , a standard practice is to define the cost function that works best in solving the problem and then to figure out which algorithm would work best in the optimisation of the algorithm in order to define the hypothesis space that one would like to test . When someone works with algorithms that do not allow the cost function that one wants , one can still indirectly influence their optimisation process by fixing their hyper-parameters and selecting your input features with respect to the cost function . Finally , when someone has gathered all the algorithm results , then one may evaluate them by using the chosen cost function and then decide over the final hypothesis with the best result from the chosen error function .

 

*  Whenever an algorithm learns from a dataset ( combination of multiple data arranged in attribute order ) , the cost function associated with that particular algorithm guides the optimisation process by pointing out the changes in the internal parameters that are the most beneficial for making better predictions . This process of optimisation continues as the cost function response improves iteration by iteration with a process of improvised learning which of course is a result of iterative learning of the algorithm . When the response stalls or worsens , it is time to stop tweaking the algorithm's parameters because the algorithm is not likely to achieve better prediction results from there on . And when the algorithm works on new data and makes predictions , the cost function helps to evaluate whether the algorithm is working correctly

 

In Conclusion .. Even if the decision on undertaking any particular cost function is an underrated activity in machine learning , it is still considered as a fundamental task because this determines how the algorithm behaves after learning and how the algorithm would handle the problem that one would like to take up and solve . It is suggested that one should not go with the default options of cost functions but rather should ask oneself what should be the fundamental objective of using such a cost function which would yield their appropriate result

 

 

 

Last modified: 00:26