Koja je svrha algoritma zasnovanog na slučajnom izbacivanju?

• Da kreira backup podataka

• Da pronađe optimalno rešenje

• Da nasumično eliminiše elemente iz skupa podataka

• Da sortira podatke u rastućem redosledu

Which method is an example of random elimination?

• Bubble Sort

• Monte Carlo Method

• Binary Search

• Linear Regression

In what scenarios are random algorithms particularly useful?

• When there are too many possible solutions

• When all solutions are equally optimal

• When data is structured

• When exact solutions are easy to find

What is a classic goal of random algorithms?

• To eliminate all elements

• To find a solution that is good enough

• To create a new dataset

• To achieve the best possible outcome

What type of problems can the Monte Carlo method help solve?

• Simple arithmetic problems

• Complex problems requiring random sampling

• Sorting algorithms

• Data encryption

What is an example of a situation where random elimination might be applied?

• Choosing a random number

• Sorting a list of names

• Eliminating candidates for a job interview

• Calculating averages

How does random feature elimination improve machine learning models?

• By selecting only the most important features

• By adding more features

• By eliminating all features

• By increasing the dataset size

What is a benefit of using random algorithms in big data?

• They eliminate the need for data analysis

• They require more data processing

• They reduce computational complexity

• They always find the optimal solution

What does the term 'feature elimination' refer to in machine learning?

• Removing unnecessary features to improve performance

• Changing the model architecture

• Increasing the number of data points

• Adding new features to a model

What is a key characteristic of random algorithms?

• They are deterministic

• They always produce the same result

• They rely on randomness for decision making

• They require no input data