gender;age;continent;exp_ai;trust_ai;workplace_ai;risk_awareness;SVM;ANN;Linear Regression;XANN;Random_Forest;Decision_Tree;Use Case;Expl;RT1;RT2;T1;T2;RC1;RC2;Comp;Remarks on Model
1;2;3;1;3;4;3;6;4;5;5;5;5;WINE;SVM;False;False;True;True;True;True;0,67;how the formula works
2;3;4;2;2;3;4;4;5;5;5;6;7;WINE;RF;True;True;True;True;False;False;0,67;All of the explanations need to be used as a  twofer.  By using 2 of the different explanations for the model I would feel I had the best handle on it.
1;2;3;2;3;4;2;6;5;4;7;6;5;WINE;LR;True;True;False;True;Trues;True;0,83;not sure
2;3;1;3;3;4;4;7;2;6;3;5;6;WINE;SVM;False;True;Trues;True;Trues;True;0,83;Nothing.
1;2;3;2;3;3;3;7;3;6;5;6;6;WINE;DT;True;True;True;True;True;True;1;Everything was clearly explained.
2;1;3;1;3;4;4;3;3;5;6;5;6;WINE;XANN;True;True;True;True;True;True;1;Subjective traits such as color or scent
1;2;3;3;4;5;3;6;2;3;5;6;5;WINE;DT;False;True;True;True;Trues;True;0,83;It's very basic but the graphs shown above are missing a lot of detail on the X axis to allow easier interpretation of the graph on such a small scale. It's a linear representation but almost looks logarithmic. Apologies for the limited explanation.
2;2;3;1;3;3;4;5;5;4;3;3;5;WINE;DT;False;False;False;False;False;False;0;very small text
2;2;3;1;3;4;4;5;2;5;6;3;2;WINE;Blackbox ANN;False;False;False;False;False;False;0;the lack of graphical information for the results on the wine quality makes this difficult to digest
2;2;3;1;3;3;3;6;5;5;5;5;5;WINE;DT;False;True;True;False;False;True;0,5;quality and authentic results
1;2;3;3;3;4;3;4;2;5;6;5;3;WINE;XANN;True;True;True;True;False;True;0,83;I didn't miss anything.
1;3;3;1;3;3;1;4;2;5;6;6;5;WINE;XANN;True;False;True;True;True;True;0,83;The problem with this algorithm was the difficulty to see exactly how changing each individual element would effect the overall score. While it shows that residual sugar really increased the quality of the wine, how much is too much before it adversely effects the wine.
2;3;3;1;3;3;2;5;2;4;6;3;5;WINE;RF;True;True;False;True;True;True;0,83;What levels of each are important in wine
2;3;3;5;4;4;3;7;6;6;6;6;6;WINE;SVM;True;False;True;True;True;True;0,83;Nothing
2;2;3;1;2;3;3;4;4;6;5;7;7;WINE;LR;False;True;False;True;True;True;0,67;This, to someone who is not minded in this way, is somewhat complicated to understand but it does give the very basics as to what chemical ingredients make a quality wine. Perhaps some further graphs would be useless to feel wholly confident.
2;2;3;1;3;3;3;6;4;4;6;5;5;WINE;Blackbox ANN;False;False;False;True;True;True;0,5;I need to learn more about the model and I need to practice some activities related to the model. For me its really new so I am a beginner and I need to improve my skills in this subject.
1;3;3;1;4;5;4;6;5;5;5;6;6;WINE;XANN;True;False;True;True;True;False;0,67;The interaction between all the components
1;3;3;1;3;3;4;3;1;6;5;7;7;WINE;DT;True;True;True;True;True;True;1;More interconnected tree decisions.
1;3;4;2;2;3;3;6;4;6;6;6;5;WINE;XANN;Fale;False;False;Fales;True;taste;0,33;After thinking it over quite throughly I find I'm confident in trusting the model as a support system as is
1;2;3;1;3;3;3;5;6;6;6;5;6;WINE;XANN;True;True;True;True;Trues;True;1;i am confident in using this model because it provides a the importance of each feature in the wineand displays the whether it has a negative or a pistive effect on the quality of the wine.
1;2;3;1;2;3;3;6;5;6;6;5;4;WINE;Blackbox ANN;False;True;False;False;False;False;0,17;more explanation
1;2;3;3;3;4;4;6;3;6;6;5;4;WINE;SVM;False;False;True;True;False;True;0,5;I feel like it could have a way to insert what kind of wine you wanted and it would give you the needed parameters.
1;3;3;1;2;4;4;6;1;6;6;4;3;WINE;XANN;True;True;True;True;False;True;0,83;I really like these explanations, and as analyst, it gives me courage to explore ML in my business! I think it's a little complicated and scientific for the lay person, so if anything a more visual presentation would be best. Perhaps more clearly show the positive and negative influences of each element on the overall quality - the red/blue line isn't that clear!
1;2;3;2;4;4;3;5;2;6;6;6;6;WINE;SVM;False;True;True;True;True;True;0,83;After trying to understand that algorithm, I found out that I'm not really that much into AI and I do not really understand that model. I do not know at what point I stop understanding it, so I can not really help. I'm so sorry.
2;2;3;2;3;3;3;6;2;6;5;5;5;WINE;SVM;False;True;False;False;True;True;0,5;The optimal level or ratios of the different components.
1;4;3;2;3;3;2;3;4;5;5;5;5;WINE;DT;True;True;True;True;True;True;1;I would like to see if the ML conclusions are the same as traditional methods of accessing wine quality
1;3;3;1;3;4;2;5;1;6;5;3;6;WINE;DT;True;True;True;True;True;False;0,83;An explanation of the small charts would make me feel more confident.
2;2;3;2;2;3;4;5;1;5;6;4;4;WINE;XANN;False;False;True;True;False;True;0,5;I feel the language used it too scientific for this to ever actually be used on a large scale. Consumers of technology are most comfortable when things are easily digested. 
1;1;3;1;4;3;3;5;4;6;5;2;2;WINE;SVM;True;True;False;True;True;True;0,83;Possibly colours that are different rather than close together to easily show on each graph 
1;2;3;1;3;3;4;2;1;6;4;4;3;WINE;LR;True;False;True;False;False;True;0,5;type of wine, country of origin
1;5;3;1;1;2;2;3;5;4;6;3;4;WINE;DT;False;False;True;True;True;True;0,67;I don't think the algorhythm really explained the process
2;4;3;1;2;2;3;2;3;4;5;2;2;WINE;Blackbox ANN;False;False;False;True;True;True;0,5;The visual model prediction in the black box
1;2;3;1;4;3;2;5;3;3;3;6;3;WINE;Blackbox ANN;False;False;False;True;False;True;0,33;Having an explination of the results would have helped and given me enough feedback to adjust the input 
1;1;3;1;5;5;5;3;5;3;5;5;4;WINE;RF;True;True;True;True;True;True;1;For me its perfect
1;2;3;1;3;3;3;5;3;4;6;5;6;WINE;XANN;True;True;True;False;True;False;0,83;pH
1;2;3;2;4;4;4;2;2;6;5;4;4;WINE;LR;True;True;True;True;False;True;0,83;with the 3D nature of the charts they were quite difficult to read. If they were interactive and could be turned then I could read them a lot more accurately. 
1;4;3;1;2;2;2;6;4;6;6;5;7;WINE;DT;True;True;True;True;True;True;1;I'm happy with the explanation here.  It is easy to understand from a non-technical perspective - the process can be followed and understood manually, similar to a decision tree for identifying a type of bird or plant.    At this point, I've also reconsidered how I can improve the quality of my own wine.  I think I could decrease volatile acidity and increase sulphates.
1;4;3;2;3;4;3;2;1;3;7;6;6;WINE;DT;True;True;True;False;True;True;0,83;I don't miss anything
1;2;3;2;4;2;4;5;2;4;6;6;5;WINE;XANN;False;True;True;True;True;True ;0,83;Nothing
2;2;3;1;4;4;3;3;5;3;7;5;5;WINE;XANN;True;True;True;True;True;True;1;deeper understanding ad knowledge of AI
1;2;3;1;3;3;4;5;5;3;4;5;4;WINE;SVM;True;True;False;True;True;False;0,67;I dont know
1;2;3;1;2;5;2;5;2;2;5;3;3;WINE;Blackbox ANN;True;False;False;False;True;True;0,5;I need to know the conclusion was made.
1;2;3;2;3;3;5;4;4;4;5;4;1;WINE;SVM;False;False;False;True;True;True;0,5;
1;3;3;1;3;3;3;3;2;6;6;4;6;WINE;DT;True;True;True;True;True;True;1;There is not anything I miss. I feel confident in using this model as a support system.
1;4;3;3;3;3;2;6;1;6;6;5;5;WINE;LR;False;True;True;True;True;False;0,67;I miss clarification of the deviation rate enabling me to ensure my decisions are as accurate as possible.
2;2;3;5;3;4;3;5;6;6;6;5;5;WINE;SVM;False;False;True;True;True;True;0,67;I feel more confident in this support system as you can visually see the results which is easier to track where the highest quality is. However, it doesn't show every aspect of every chemical so you're not getting full results.
2;2;3;2;2;2;3;5;5;4;3;6;5;WINE;SVM;False;False;False;True;False;False;0,17;the process
1;2;3;3;4;4;3;3;5;6;4;5;3;WINE;XANN;False;True;True;False;True;True;0,67;i need more information in the finals results where apears the read and blue indicators
2;2;3;1;2;3;3;3;1;6;7;6;2;WINE;XANN;False;True;True;True;True;True;0,83;Maybe more examples to help me fully understand what is going on. And explain how to interpret the graphs better so if you change one thing, what happens to the others? Also how likely the system is to make a mistake 
1;2;1;1;3;2;3;4;5;4;5;5;4;WINE;Blackbox ANN;False;False;False;True;True;True;0,5;The accuracy of the output is a great influencing factor.
1;5;3;1;3;3;2;3;4;5;5;3;5;WINE;Blackbox ANN;False;True;False;False;True;True;0,5;.
1;2;3;2;4;4;4;5;5;5;5;5;5;WINE;DT;False;True;False;False;False;True;0,33;Here there is a lot of informations with graphs
1;3;3;2;3;3;4;5;1;5;6;4;3;WINE;XANN;True;True;True;True;True;True;1;It is a clearer looking model without slot of mess making it easy to understand.
2;4;3;4;4;4;4;6;5;3;6;6;2;WINE;SVM;False;False;False;True;True;True;0,5;nothing
2;2;4;2;4;4;4;5;6;6;6;5;4;WINE;LR;False;False;True;True;False;True;0,5;The residuals
1;2;3;2;4;5;2;7;7;5;4;6;7;Bakery;DT;True;True;True;True;False;True;0,83;better explenation
1;1;3;3;3;1;4;4;1;2;7;2;5;Bakery;XANN;True;True;True;True;False;Trues;0,83;There is too much information for me to fully take in. I would need the full explanation and then a short summary of the model. 
2;2;3;2;3;2;4;4;2;5;4;5;3;Bakery;LR;False;True;False;False;True;True;0,5;I miss a qui-square so i can check the median.
2;2;3;1;3;4;4;5;4;5;2;3;6;Bakery;RF;True;False;False;True;False;False;0,33;Why temperature would have a large affect on the number sold
3;3;5;1;3;4;2;4;4;5;3;2;1;Bakery;LR;False;False;True;False;True;True;0,5;more cleaning of the dataset, minimeze the feature compilations to a manageable size for a human to explain. filter out the useless factors.
1;2;4;1;2;2;3;2;2;5;3;2;1;Bakery;LR;False;False;True;True;True;True;0,67;I think I'm confident using linear regression model for the bun production decision without any extra explanation.
1;2;3;1;4;4;3;4;4;5;5;5;5;Bakery;RF;True;True;True;True;True;False;0,83;Prostszego systemu wtedy mógłbym lepiej zrozumiec cały ssytem
1;2;3;1;5;5;4;2;3;1;5;6;5;Bakery;RF;False;False;True;True;False;Trues;0,5;My only doubt is if it's secure to use the IA, because I fear it could make some extreme and not real scenarios that badly influence the estimations. For exemple, it thinking just because it's not an holiday and heavely raining during school time that the sales could drop harder than they normally do
1;2;4;1;3;2;4;6;5;6;5;5;6;Bakery;LR;True;True;True;True;False;True;0,83;I would want more data points to show a better graph.
1;2;3;3;3;4;3;5;6;4;6;6;5;Bakery;RF;True;True;True;True;True;True;1;Regardind the  buns  example, I don't think that there's anything missing, so I guess I would use it with no problems.
2;1;3;1;4;3;4;3;2;5;5;5;3;Bakery;XANN;True;True;False;False;True;Trues;0,67;Why each feature is more or less relevant
2;4;3;2;2;3;2;4;2;5;6;3;3;Bakery;LR;True;True;False;True;True;True;0,83;I don't know
2;2;4;5;2;3;4;6;3;5;2;3;3;Bakery;RF;True;True;True;True;True;True;1;I would like clearer visuals and a legend to accompany each visual explanation
2;2;4;2;3;3;4;5;6;5;6;6;7;Bakery;DT;True;False;True;False;True;Trues;0,67;Missing consideration of slow or low sale days, missing consideration of frequent or regular customer basis/reacurring sales
2;2;3;1;2;3;3;5;3;4;5;6;5;Bakery;SVM;True;True;True;True;False;False;0,83;general statistics such as the model's accuracy rate based on previous results
2;2;3;1;2;3;3;6;3;5;3;4;5;Bakery;SVM;True;False;False;False;True;True;0,5;clarity in charts, for example, clear background would be better for reading data on tables
2;2;3;1;3;3;3;5;3;4;6;4;3;Bakery;Blackbox ANN;True;True;True;True;True;True;1;I woukd need to learn more about the system.
2;2;3;1;3;3;3;4;2;5;6;6;5;Bakery;XANN;True;False;False;True;False;Trues;0,5;I think the model explanation shows the correct amount of information as to not overwhelm the viewer
2;4;3;5;4;4;2;3;6;4;4;5;5;Bakery;DT;True;False;False;False;False;False;0,17;sales of the butchers
1;3;3;2;4;2;3;5;5;5;4;4;5;Bakery;RF;True;True;True;True;True;True;1;A more simplistic evaluation to make it more usable 
1;2;3;3;4;4;3;3;2;5;6;2;3;Bakery;XANN;True;True;False;False;True;False;0,5;nothing really, it's quite clear and understandable
2;3;3;1;3;3;3;2;3;5;2;2;3;Bakery;XANN;True;True;True;True;True;True;1;I miss an explantion of the bolded words (and abbreviations), as well as an explanation of how to interpret the influence of the features. Overall the explanation is difficult to read (both the text and the presentation/layout). I had a hard time determining what the actual difference between the models was, as they were all equally poorly explained. 
1;1;6;2;4;5;3;6;3;5;6;5;5;Bakery;XANN;True;True;True;True;False;False;0,83;We miss the knowledge of customer satisfaction
2;3;3;2;3;2;3;5;3;6;6;4;6;Bakery;DT;True;True;True;True;True;True;1;I dint quite understand the diagram as it shows in the second example . The algorithm is not clear enough for me .
2;2;3;1;3;2;4;6;2;7;6;4;5;Bakery;LR;True;True;False;False;True;True;0,67;I feel there is enough information provided within the model to make a confident, informed decision. 
1;3;3;1;3;3;2;1;2;1;2;1;4;Bakery;Blackbox ANN;True;True;False;False;False;True;0,5;There is no explanation, but compared to the other 'overly complex' explanations, I felt this one was best. It would be better to have a little data though, but not an overwhelming amount.
1;2;3;1;3;4;4;2;1;1;6;3;4;Bakery;Blackbox ANN;True;True;True;True;False;True;0,83;Maybe an actual graph that would explain and break down the number at the end. 
2;2;3;1;3;3;4;3;5;5;5;3;6;Bakery;DT;False;True;True;True;True;False;0,67;nothing comes to mind, i feel all relevant information is provided. 
1;3;3;2;3;3;4;5;4;3;3;6;5;Bakery;RF;False;False;True;False;False;False;0,17;This model has a structure that most brain can understand than the others, therefore it feels more safe to use it despite the difference of estimated and actual since through the process of time it can lead you to safer results, and since we are talking about bread and steaks that the profit margin is very big there is space to use it
1;3;3;1;3;3;4;6;3;5;5;6;5;Bakery;XANN;True;False;True;False;True;Trues;0,67;The system only takes into account the weekly average demand and doesn't have human reaction to account for different situations etc.
1;2;3;1;4;4;3;4;2;6;5;5;5;Bakery;LR;True;True;True;True;True;Trues;1;More data to be injected into the model would make me feel more confident and some trial data as well as to show whether they should the corresponding number of buns as suggested 
1;2;6;2;4;2;3;1;1;5;6;3;2;Bakery;XANN;True;True;True;True;False;Trues;0,83;I'd love to see how they arrive at the result, step by step in order to be convinced that the model can be used as a support system.
1;2;3;1;3;3;3;5;4;6;6;5;6;Bakery;LR;True;True;True;True;True;True;1;i chose this system because i belive that we can easily have a linear correlations between the data that would allow us ti have an accurate prediction
1;2;3;1;3;4;2;4;2;5;7;6;6;Bakery;XANN;True;True;True;True;True;Trues;1;How the algorithm decides the average impact of each factor on the output magnitude and how reliable this weighted impact is if the dataset provided is small
1;2;3;1;3;3;3;3;4;5;6;6;6;Bakery;XANN;False;False;False;False;False;True;0,17;I think the model explanation is missing more information in the results section, where the graphical representation of the data could be explained in more detail - e.g. what does it mean by positive and negative influence? Also, what do the different lengths of variables on the positive/negative scale mean?
2;3;3;1;3;3;2;6;2;3;5;2;4;Bakery;Blackbox ANN;True;True;False;False;True;True ;0,67;i find this confusing
1;3;3;1;2;3;3;2;4;3;3;4;4;Bakery;DT;False;True;False;True;False;False;0,33;[deleted, due to GDPR]
1;3;3;1;5;5;3;5;3;6;3;3;3;Bakery;LR;True;True;True;True;True;True;1;I did not realise this system does not take into account some much more important factors
2;2;3;1;3;3;4;6;3;5;6;6;6;Bakery;XANN;True;False;False;True;False;True ;0,5;More data, more thorough explanation
2;1;3;1;2;3;3;5;2;5;4;6;4;Bakery;RF;True;False;False;False;False;False;0,17;
1;3;3;1;4;4;3;5;5;5;3;5;6;Bakery;Blackbox ANN;True;True;False;True;True;Trues;0,83;I would need easier explanations as these models are quite abstract and hard to understand
1;2;3;1;3;3;3;1;2;2;5;4;2;Bakery;XANN;False;False;False;False;True;True;0,33;Further explanation as to how the specific prediction is derived from the different features. Overall the the language for all explanations is far too technical for a layman to make any sort of informed decision on which model to use.
2;4;3;1;3;3;2;5;4;3;5;2;2;Bakery;Blackbox ANN;True;True;True;True;True;True;1;outside factors such as weather and temperature
1;2;3;3;4;4;3;4;3;5;5;5;6;Bakery;DT;True;True;False;False;False;False;0,33;it was ok
1;2;3;1;3;3;3;5;2;3;6;6;6;Bakery;Blackbox ANN;True;True;True;True;True;True;1;an explanation would make this model a strong support system
2;4;3;1;3;3;3;5;3;4;5;4;5;Bakery;DT;True;True;True;True;True;True;1;I can not answear with certainity.
2;4;3;3;3;3;1;3;1;5;6;6;6;Bakery;DT;True;True;False;False;True;True;0,67;Would prefer that equivalent year-month (eg Jun20 v Jun19) data comparison included  With stronger reference to weekday impact also 
1;1;3;4;4;5;3;4;2;2;2;4;4;Bakery;DT;True;True;False;False;False;True;0,5;Concise information on the second photo and what the chart is showing instead of just the number 0.2,0.4 etc
2;2;3;1;3;3;3;3;2;3;4;4;4;Bakery;RF;True;True;True;False;Days;True;0,67;A more basic vocabulary and some more definitions. It was alot of information at once and it was hard to workout what different things meant. I think I understood it, but there was a high level of guessing as well due to being unsure of meanings.
1;2;3;1;2;2;2;3;2;6;6;5;4;Bakery;XANN;True;True;True;True;False;True;0,83;I would like for it to be a year round dataset, would make it so seasonal fluctuations with vacations would be easy to prepare for.
2;2;3;1;3;3;3;5;2;2;6;6;4;Bakery;XANN;True;False;True;True;False;False;0,5;positive and negative influence
1;3;3;3;2;2;3;5;3;5;4;4;6;Bakery;DT;True;True;True;True;True;True ;1;i miss to see the operational working in the concrete case. in fact one thing is tehory and another is practice. With some help and direct understandings that should be right btw
2;2;3;1;3;3;3;6;2;7;4;5;6;Bakery;LR;True;True;True;True;True;True;1;all models lacked information on how many were produced in the other weeks and what amount of buns were wasted. 
2;3;3;1;2;3;3;2;1;6;5;5;4;CMAPSS;SVM;True;True;True;True;False;True;0,83;Not enough examples and numbers to interpret
2;4;3;1;3;3;3;6;4;6;7;5;5;CMAPSS;XANN;True;True;True;False;True;True;0,83;I feel that I am missing more data.   I would have liked more data and more information about the SVM. I would have liked more information on all of the choices to be honest. I found the task somewhat complicated as I have very little knowledge about this subject 
1;3;3;2;4;4;5;5;5;4;4;5;5;CMAPSS;XANN;True;True;True;True;False;False;0,67;Not sure I understand the question. I do not think there is something I missed in the explanations.
1;2;3;2;4;4;4;2;3;4;5;3;3;CMAPSS;RF;True;True;False;False;False;True;0,5;It's really hard for me to say as an outsider, because these things I'm completely unaware of. I have no knowledge in this specific area, I'm reading it for the first time today, so I know nothing of it. I would say you have provided a lot of data, a lot of explanations, so if I had to guess (because I don't know any better), I would say yes, I would feel confident using this model. 
1;2;4;2;2;4;3;5;1;6;5;7;6;CMAPSS;RF;True;True;True;True;False;True;0,83;nothing.
2;2;3;2;2;4;3;5;5;5;5;4;4;CMAPSS;LR;True;False;True;False;False;True;0,5;I don't have a lot of background, if any, with machine learning but I would have loved to be told more details on graphics. Some of them are very complex that I doubt my answers. I'd say that, for the most part, a little bit of randomness (as provided with the random forest) should be allowed and help the mean results, as there is always some level of uncertainty for every sensor.
2;3;3;2;4;4;4;6;2;6;6;4;5;CMAPSS;XANN;True;True;True;False;False;True;0,67;I chose this model because It looked easy to understand but it is not, at least for me.
2;2;3;1;3;2;1;1;2;1;1;1;3;CMAPSS;DT;True;False;False;False;True;False;0,33;The hidden layers 
2;3;3;2;3;3;3;4;4;4;4;4;4;CMAPSS;XANN;True;False;False;False;False;False;0,17;"I didn't understand the explanations in the first place. They are too complex, too complicated; the graphic shown is too small, I can't make anything out of it."
1;4;6;2;3;5;2;1;1;1;1;1;1;CMAPSS;XANN;True;True;True;False;True;False;0,67;I don't feel confident at all.
1;2;1;1;3;5;5;5;4;6;4;5;5;CMAPSS;XANN;True;True;True;False;False;False;0,5;I don’t understand this model at all. 
1;2;3;1;4;4;3;5;5;7;4;5;6;CMAPSS;XANN;True;True;True;False;False;True ;0,67;Some Form of confidence in the predictions eg 95% confidence interval on influence or maybe an error estimate eg influence is negative and 100+-5 
2;3;3;1;2;3;2;6;4;5;5;4;4;CMAPSS;RF;True;False;True;False;False;False;0,33;If simulations were done to prove that this data is useful or not
2;2;3;2;4;5;2;4;3;5;5;5;4;CMAPSS;RF;True;True;True;False;False;True;0,67;Not confident 
1;2;3;4;5;5;3;6;1;6;5;3;4;CMAPSS;SVM;False;False;True;False;False;True;0,33;I would maintain my decision and use this model as a support decision.
1;3;3;2;3;4;3;4;2;5;5;3;4;CMAPSS;XANN;True;True;True;False;False;True;0,67;I would use other colours in the graphics for example: red which turns green.
1;2;3;2;3;4;2;6;1;6;6;6;7;CMAPSS;LR;True;True;True;True;False;False;0,83;Although I can understand the concept and find it reasonably readable, some other people may find the ML/AI complicated, and may possibly require a simpler language or some analogies to grasp the concept.
2;2;3;1;2;1;2;6;3;5;6;4;4;CMAPSS;XANN;True;True;True;True;False;True ;0,83;I actually believe that this particular model seems the more reliable to show the (average) R.U.L. of the specific turbine. The tree/forest ones seem to me too unreliable due to their statistic process, the networks seem unfitting for this kind of analysis as they seem much more oriented on trial and error process of thought and the vector, well, I am not that sure how the vector can process the data correctly by looking at the results.
2;2;3;1;3;3;4;5;2;5;4;5;3;CMAPSS;SVM;True;False;True;False;True;True;0,67;I think more examples would be useful with real life scenarios so it’s clear to the consumer just how helpful the model is. This would make it more relatable. 
1;2;3;2;3;4;4;6;5;6;6;6;6;CMAPSS;XANN;True;True;False;True;False;True;0,67;I think the model should explain how the combination of every feature is done and how the final result is reached.
1;4;3;1;3;4;3;6;6;6;6;6;6;CMAPSS;RF;True;True;True;True;False;True;0,83;The specific and detailed mechanism of the AI work
1;2;3;2;4;3;4;5;3;5;5;4;5;CMAPSS;XANN;True;True;True;False;True;True;0,83;There will be random events that could occur and so not be predicted and any human interaction that may occur is prone to error.
1;4;3;1;4;3;2;6;4;6;7;6;5;CMAPSS;XANN;True;True;True;True;False;True;0,83;Further explanation if the result was calculated for the best or for the worst possible conditionts.
1;2;3;1;4;4;4;5;2;5;5;3;3;CMAPSS;XANN;True;True;True;False;False;True;0,67;Reliability in real cases of the model
2;3;3;1;3;1;3;3;6;5;6;1;1;CMAPSS;Blackbox ANN;True;True;True;True;False;False;0,67;what is this 104.87 represent, what are the units of measurement?
1;2;3;1;4;4;3;7;2;6;7;5;2;CMAPSS;XANN;True;True;True;True;False;True;0,83;Nothing
2;2;3;1;3;3;3;4;6;4;6;6;6;CMAPSS;SVM;True;False;True;False;True;True;0,67;Maybe this model applied to a short  common knowledge  example, clearly showing its predictability/forecasting ability.
2;2;3;2;3;5;4;5;7;3;6;5;4;CMAPSS;XANN;True;True;True;True;True;True;1;Applied to a situation i am familiar with.
1;2;3;1;4;4;4;5;2;7;5;3;6;CMAPSS;LR;True;True;True;False;True;True;0,83;More informariam about the data, such as, quantity and data distribution
1;2;1;1;3;4;2;3;5;6;4;4;4;CMAPSS;LR;True;False;True;False;False;True;0,5;Some explanations for some technical terms.
2;2;3;2;4;4;3;3;3;6;5;4;5;CMAPSS;LR;True;False;False;True;True;True;0,67;The graphs are difficult.
1;2;3;1;4;4;2;6;2;6;5;3;5;CMAPSS;LR;True;True;Tue;True;True;False;0,83;How the feature combinations are added together
1;3;3;2;3;4;3;5;5;6;5;2;4;CMAPSS;LR;True;False;True;False;True;True;0,67;I don't know.
2;2;3;1;3;3;3;5;1;5;5;5;6;CMAPSS;XANN;True;False;True;False;Ture;False;0,5;More information.
1;3;3;1;2;1;3;6;2;5;4;4;4;CMAPSS;SVM;True;True;True;False;True;True;0,83;Would like to see more raw data
1;2;3;1;3;4;3;2;1;5;2;5;5;CMAPSS;LR;True;True;False;False;True;True;0,67;The detailed algotihm with values and critical values.
2;2;3;1;3;4;3;5;1;3;6;3;2;CMAPSS;SVM;True;False;False;True;False;True;0,5;A simplified visual representation
2;3;3;2;4;4;3;6;3;6;6;6;4;CMAPSS;RF;True;True;True;False;True;True;0,83;I understand it far less that I thought I did. The color model seemed clear, but perhaps somethin easier like the artificial neural network (with a clear blu = positive and red= negative) would be better. I don't know if the colour mixing or being very separate is better or worse in this model. I also know nothing about AI, perhaps knowing at least something is necessary to understand every one of the models.
1;4;5;2;2;2;4;6;2;6;5;5;3;CMAPSS;RF;True;True;True;False;False;True;0,67;Personally an worked example to gain experience of interpreting the model would help to give more confidence in interpreting it.
1;3;3;1;4;5;3;5;2;7;5;6;6;CMAPSS;LR;True;True;True;True;True;True;1;yes
2;1;3;2;3;4;3;3;3;4;5;6;5;CMAPSS;RF;True;True;False;True;True;True;0,83;I would feel confident using linear regression because its something I use day to day and I'm familiar with the modelling techniques for it. It does make me slightly biased, however I do think showing correlations between the sensors and various data is the most useful way of predicting performance. The more data thats added, the stronger the model becomes. 
2;2;3;5;2;3;3;5;7;5;6;3;6;CMAPSS;XANN;True;True;True;False;True;True;0,83;I don’t real know to be honest, for me there’s something about the way the data is showned
2;5;3;2;2;3;1;5;4;2;6;5;4;CMAPSS;XANN;False;True;True;False;True;False;0,67;The explanation has to start simple and build up in complexity like this one. Additionally, the ANN explanation used the neuronal networks as an explanation base which made it easier to understand from the beginning as participants may have prior knowledge of these networks making it easier to relate it to the data presented.
1;2;3;1;3;3;2;5;2;5;5;2;2;CMAPSS;SVM;True;True;True;False;True;True;0,67;what the sensors are for and what they do
1;2;4;2;4;3;3;5;3;6;3;6;3;CMAPSS;LR;False;True;True;True;True;True;0,83;
2;2;3;2;3;4;4;5;2;7;3;3;2;CMAPSS;LR;True;True;True;True;True;True;1;Ability to use two or more and get the RUL independent of ither sensor
1;2;3;1;4;4;4;3;1;5;5;4;6;CMAPSS;DT;True;False;True;True;True;True;0,83;a better explanation of the function of the sensors/settings.
1;3;3;1;2;2;2;4;4;4;5;4;4;CMAPSS;XANN;True;True;True;False;Trues;True;0,83;Showing a general result as a resume of how every sensor is showing positive or negative results.  Also there is a problem with this support system in this scenario, the numbers are far too small to be read even with the zoom item you supported us. This problem is easily solved and because of that reason I did not have that in count in order to make my decision.
1;2;3;1;2;2;2;4;5;5;6;6;5;CMAPSS;XANN;True;True;True;False;True;True;0,83;
1;2;3;1;4;4;3;5;2;4;6;6;4;CMAPSS;SVM;False;True;True;False;True;True;0,67;With this one the processing which is hidden, the original dataset so show why/how it comes to the 104.87 result. So you can also see/check to see if you agree with the raw data rust small bits of it.
1;4;3;2;4;4;3;5;6;6;5;6;6;CMAPSS;XANN;True;True;True;False;Trues;True;0,83;How much data with n-feature can algorithm support?
2;4;3;1;2;3;3;5;1;5;6;3;2;CMAPSS;LR;True;False;True;False;True;True;0,67;I would fee more confident having a few more examples of the predictions.
1;2;3;1;4;4;3;5;6;6;4;5;6;CMAPSS;XANN;True;True;True;False;True;True;0,83;I would like to see the complete dataset for all of the sensors and their comparisons to all of the possible settings.  Then I could understand e.g. whether some sensors give much higher or lower readings than others and where the overall RUL result is coming from a bit more comprehensively.
1;3;3;2;2;2;3;5;2;5;5;5;4;CMAPSS;LR;True;False;True;False;True;True;0,67;I feel confident in using this model as support system. 
1;3;3;3;3;3;4;5;4;6;6;2;5;CMAPSS;Blackbox ANN;False;True;True;False;False;True;0,5;In hindsight I'd have liked to have seen all of the different measures in one plot.  The plot of the different relative contributions provided by the other algorithms was actually very useful.
2;3;3;3;3;2;3;6;2;5;4;6;5;CMAPSS;SVM;True;False;True;True;True;True;0,83;no
1;2;3;1;4;5;1;2;3;5;5;6;6;CMAPSS;RF;True;True;True;True;True;True;1;I miss control values to compare the values obtained in the specific turbine.