Search Results For F1 2020
The 2020 FIA Formula One World Championship was the motor racing championship for Formula One cars which was the 71st running of the Formula One World Championship.[a] It marked the 70th anniversary of the first Formula One World Drivers' Championship.[1] The championship was recognised by the governing body of international motorsport, the Fédération Internationale de l'Automobile (FIA), as the highest class of competition for open-wheel racing cars. Drivers and teams competed for the titles of World Drivers' Champion and World Constructors' Champion, respectively.
Search results for f1 2020
Twenty-two Grands Prix were originally scheduled for the 2020 World Championship.[2] However, the COVID-19 pandemic resulted in numerous race cancellations and postponements. A rescheduled calendar consisted of seventeen Grands Prix, nine from the original 2020 calendar and eight other Grands Prix, while the other thirteen original 2020 races were cancelled. This also meant that the season started with two races in Austria, and later on in the season there were also two races at Silverstone Circuit along with two races at Bahrain International Circuit. Each race is the minimum number of laps that exceeds a total distance of 305 km (189.5 mi). Under the sporting regulations, a minimum of eight races must take place for the season to be considered a championship.[47][f]
Liberty Media initially expected that the 2020 calendar would consist of twenty-one Grands Prix and that any new races would come at the expense of existing events, but later negotiated an agreement with the teams to allow up to twenty-two Grands Prix. Several further changes were made between the 2019 and 2020 calendars, with the German Grand Prix discontinued and the Mexican Grand Prix planned to be rebranded as the Mexico City Grand Prix before it was cancelled.[70][71]
In early April, organisers of the Canadian Grand Prix announced the race's postponement.[58] Later in the month, the French Grand Prix organisers confirmed that the race would not be held in 2020,[59] and the managing director of Silverstone Circuit stated that should the British Grand Prix go ahead, it would be without spectators.[90] In May, organisers of the Hungarian Grand Prix announced that their race would use the same model.[91] The sport's plans to resume competition called for a ban on team motorhomes and a rigid testing regime to stop any outbreak of the virus.[92]
The Dutch Grand Prix was cancelled entirely in late May, with organisers of the event stating that they would prefer to host the revived race with spectators in attendance in 2021 rather than without spectators in 2020.[69] Formula One confirmed the cancellation of the Azerbaijan, Singapore and Japanese Grands Prix in June.[93] Organisers of the Azerbaijan and Singapore races cited the difficulty of assembling the infrastructure required for a street circuit as the reason for their cancellation, while the Japanese Grand Prix was cancelled because of the Japanese government's travel restrictions. In July the Brazilian, Canadian, Mexico City and United States Grands Prix were formally cancelled amidst rising virus cases and travel restrictions in the Americas.[94] However, organisers of the Brazilian Grand Prix disputed the claims of Formula One Management and were unhappy with their race being cancelled without further consultation.[95] In August the cancellation of the Chinese Grand Prix was announced,[96] followed, in October, by the cancellation of the inaugural Vietnamese Grand Prix.[65]
In March, teams agreed that the 2020 Championship could run into early 2021 to ensure the running of as many races as possible. Such a move would also ensure that eight Grands Prix could be held, over three different continents, thereby meeting the minimum number of races needed for the season to qualify as a World Championship.[102][103][104]
Ahead of the season opening Austrian Grand Prix, Red Bull launched a protest against the Mercedes F1 W11's dual axis steering, a system where the driver can adjust the toe of the car by pulling and pushing on the steering wheel. The system was found to be legal for 2020, but it would be banned by the FIA from 2021 onward.[120]
Points were awarded to the top ten classified drivers and the driver who set the fastest lap. The driver with the fastest lap had to be within the top 10 to receive the point. In the case of a tie on points a countback system was used where the driver with the best results is ranked higher, if the best result was identical then the next best result was considered. The points were awarded for every race using the following system:[152]
The Portuguese Grand Prix is the next race on the 2020 Formula 1 schedule and is scheduled to take place on Sunday, October 25. This race is set to be broadcast live on ESPN from Autodromo Internacional do Algarve beginning at 9:10 a.m. ET.
To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.
The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset.
Accuracy. Moving to global metrics having three or more entries of M as input, many researchers consider computing the accuracy as the standard way to go. Accuracy, in fact, represents the ratio between the correctly predicted instances and all the instances in the dataset:
Overall, accuracy, F1, and MCC show reliable concordant scores for predictions that correctly classify both positives and negatives (having therefore many TP and TN), and for predictions that incorrectly classify both positives and negatives (having therefore few TP and TN); however, these measures show discordant behaviors when the prediction performs well just with one of the two binary classes. In fact, when a prediction displays many true positives but few true negatives (or many true negatives but few true positives) we will show that F1 and accuracy can provide misleading information, while MCC always generates results that reflect the overall prediction issues.
After having introduced the mathematical foundations of MCC, accuracy, and F1 score, and having explored their relationships, here we describe some synthetic, realistic scenarios where MCC results are more informative and truthful than the other two measures analyzed.
Recap. We recap here the results obtained for the six use cases (Table 4). For the Use case A1 (negatively imbalanced dataset), the machine learning classifier was unable to correctly predict negative data instances, and it therefore produced confusion matrices featuring few true negatives (TN). There, accuracy and F1 generated overoptimistic and inflated results, while the Matthews correlation coefficient was the only statistical rate which identified the aforementioned prediction problem, and therefore to provide a low truthful quality score.
In the Use case B1 (balanced dataset), the machine learning method was unable to correctly predict negative data instances, and therefore produced a confusion matrix featuring few true negatives (TN). In this case, F1 generated an overoptimistic result, while accuracy and the MCC correctly produced low results that highlight an issue in the prediction.
The classifier did not find enough true positives for the Use case B2 (balanced dataset), too. In this case, all the analyzed rates (accuracy, F1, and MCC) produced average or low results which correctly represented the prediction issue.
Also in the Use case C1 (positively imbalanced dataset), the machine learning method was unable to correctly recognize negative data instances, and therefore produced a confusion matrix with a low number of true negative (TN). Here, accuracy again generated an overoptimistic inflated score, while F1 and the MCC correctly produced low results that indicated a problem in the prediction process.
Finally, in the last Use case C2 (positively imbalanced dataset), the prediction technique failed in predicting negative elements, and therefore its confusion matrix showed a low percentage of true negatives. Here accuracy again generated overoptimistic, misleading, and inflated high results, while F1 and MCC were able to produce a low score that correctly reflected the prediction issue.
In summary, even if F1 and accuracy results were able to reflect the prediction issue in some of the six analyzed use cases, the Matthews correlation coefficient was the only score which correctly indicated the prediction problem in all six examples (Table 4).
Particularly, in the Use case A1 (a prediction which generated many true positives and few true negatives on a positively imbalanced dataset), the MCC was the only statistical rate able to truthfully highlight the classification problem, while the other two rates showed misleading results (Fig. 2).
These results show that, while accuracy and F1 score often generate high scores that do not inform the user about ongoing prediction issues, the MCC is a robust, useful, reliable, truthful statistical measure able to correctly reflect the deficiency of any prediction in any dataset.
For gradient boosting and decision tree, we trained the classifiers on a training set containing 80% of randomly selected data instances, and test them on the test set containing the remaining 20% data instances. For k-NN and SVMs, we split the dataset into training set (60% data instances, randomly selected), validation set (20% data instances, randomly selected), and the test set (remaining 20% data instances). We used the validation set for the hyper-parameter optimization grid search [97]: number k of neighbors for k-NN, and cost C hyper-parameter for the SVMs. We trained each model having a different hyper-parameter on the training set, applied it to the validation set, and then picked the one obtaining the highest MCC as final model to be applied to the test set. For all the classifiers, we repeated the experiment execution ten times and recorded the average results for MCC, F1 score, accuracy, true positive (TP) rate, and true negative (TN) rate. 041b061a72