Decision Tables Column Coloring – Computer Science Stack Exchange

I learn about decision tables and am confused about the coloring in the marking scheme for a question that I am currently working on. I can not see why some columns are grayed out. Instead of adding something to my understanding, the color scheme makes me feel that I lacked an important point because I can not see any similarity between the colored columns.

The questions for which this image is the solution are: i) Complete the decision table and ii) Simplify the removal of redundancies.

Can someone please explain the coloring to me?

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Machine Learning – Decision tree from a dataset

First, I am not sure if the question fits this website. If not, please let me know and I will postpone it.

In my current class (AI) we examine decision trees. An exercise that I have is building a tree from a simple raw dataset. I can extract the first decision node based on the specified amount of data, but I do not know how to create the remaining nodes, because none of the remaining attributes have a unique answer in the specified amount of data. All the clues that get me on the right track are greatly appreciated!

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I could say that A1 is the first node in my tree because all lines are pointing to y = 1 when A1 is 1

Does anyone else agree that Stacey Abrams was a terrible decision to refute the US state?

I agree that she did not really go to the address, what was lost to the Democrats, they could do anything else with better influence, it would be nice to hear them appeal, growth, immigration, national security. What they would like to see what the Trump government is not tempting, would have liked to hear that they would say that they would go without hate and actually do their jobs, you know they act like adults and stop crying one lost choice once and for all


Machine Learning – How to split at a node in a decision tree with non-categorical data?

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co np – How can it be shown that this decision problem lies in co-NP?

Given a series of absolutely positive numbers $ a_1, …, a_n $the problem is to determine if $ lfloor n / 2 rfloor $ indices $ i_1, …, i _ { lfloor n / 2 rfloor} $ exist like that $$ frac {s_ {i_j}} {s_ {i_ {j + 1}}} = frac {s_ {i_ {j + 1}}} {s_ {i_j}} $$ to the $ 2 leq j leq lfloor n / 2 rfloor $,

How do I show that this problem is in Co-NP?

finite automata – space complexity for accepting word decision problems of DFAs

It is known that the decision problem $ w in mathcal {L} (M) $ for a DFA $ M = (Q, Sigma, Delta, q_0, F) $ is in $ mathcal {O} (| w |) $, To prove this, we assume that the successor status calculation can be done in $ mathcal {O} (1) $for example with an array of size $ Q times Sigma $,

My question is: is there a better way to save the transition function with the same runtime complexity? When $ Sigma $ is very large, the table is very memory intensive. It is even worse for NFAs that contain a list of states in each cell of the array.

It's been 30 days and I have not received a decision regarding my visa application for a visit to the British family

I am from Ghana and applied for a family visa in the United Kingdom on November 22, 2018, which was due to travel on December 21 and return on January 4, 2019.

To this day, January 8, I have nothing in front of them, not even an e-mail message. What does that mean? What are you doing with my passport? Is that a sign of rejection?

Us. Court decision in support of legal sports betting

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Opportunity expected! American law and policy makers are working on a framework for the introduction and support of the legalization of sports betting.

Last week, the United States rejected a 26-year ban on sports betting, giving the 50 states the opportunity to re-establish their policies to allow legal betting on popular competitive sports. Nevada, the only state to have been active in the market for nearly 26 years, has reported that Nevada Sports Books had generated $ 4.8 billion in operations in 2017.

Other reports from the American Gaming Association estimate US illegal sports betting is worth $ 150 billion a year. With the recent change in legislation, enthusiastic players will soon have the opportunity to participate in the thrill of sports betting in a safe and open environment.

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The legalization of sports betting will help the gaming community enjoy the betting activities, but how can we maintain the integrity of the bookkeeping? While existing online sports forecasting industries have undergone rapid development, all current underlying technologies are based on centralized algorithms, internal computer systems and winners' announcements.

Traditional online sports prediction platforms are faced with a number of problems, such as the unfairness of customers due to manipulated algorithms, fraudulent behavior of sports operators (especially for small forecasting platforms), as well as hacking attacks and false information in credit card transactions.

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The solution? Block chain technology.

With extensive industry experience, the UniGame team is at the forefront of using blockchain technology in the field of sports forecasting and is dedicated to resolving the issue. By storing the platform data in the blockchain, all users can be sure that the data is credible, traceable, verifiable, and unmanageable, eliminating background tampering.

UniGame is strategically positioned to become the market leader in this multi-billion dollar industry.
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Through strong partnerships with IP holders of global top events, seasoned team members and game licenses in multiple countries; UniGame has focused on the blockchain-based sports competition industry and laid the groundwork for the launch of its real-life application.

UniGame's global chain-based sports prediction platform covers kickboxing, football and basketball events and is licensed exclusively to the International Boxing Federation (IBF), Kunlun Fight and Glory of Heroes (GOH).

Participants may use Bitcoin (BTC) or Ethereum (ETH) in return for UniGames UniCoin (UNC) tokens. UniCoin (UNC) is now officially trading on HitBTC.

More information and a copy of the white paper can be found at

Turing Machines – What is the difference between an oracle and a decision maker in Computational Theory?

I'm just learning about Turing Reductions and just wondering if there is a difference between an oracle and a decision maker. I understand the meaning of an oracle in that we do not need to know how it gets its answer, but it will always give us the right answer.

An oracle for a language B is an external device that can report if a string w is a member of B.

That's exactly the same as a decision maker. Is there a difference between the two?