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The social network for betting. Find experts you trust on the best you want to make, with complete historical statistics for their tips in all leagues and sports.

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Sorry, your browser isn't supported by audioBoom. Please try one of our supported browsers. The incentive behind the prize money is to ensure that users of bettingexpert.

As part of his prize for winning the competition, Nemanja was flown out to Denmark to meet the Better Collective team and be shown the bright lights of Copenhagen, where he was treated to a traditional Danish lunch and a canal trip to see the city.

Guillaume Mazella Head of Content Production at Better Collective told TotallyGaming. He was ranked Tipster of the Month twice in the same judging period within our English speaking community, and earned prizes for placing within the top three for Tipster of the month on 18 separate occasions across 2 different language communities.

If you are looking for some tipster advice, Ninca90 is definitely one to follow. Furthermore, the platform that is provided by Bettingexpert continues to give tipsters an unrivalled opportunity to prove their credentials as a top tipster. ODDSbible promotes honest betting banter with the Senet Group Could the World Cup provide the perfect platform for mobile betting. DraftKings expands into the world of live streaming Simplicity and speed rules for mobile focused operatorsDegree 53 expands into Manchester City Centre LADbible is revolutionising the world of online sports betting adsInBet Games extends market reach through Altenar partnership SportPesa and Microgame combine for Italian expansion Gambling.

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VAT number: 843 8456 01. Registered in England and Wales. Registered address: Bedford House, Fulham High Street, London SW6 3JW, United Kingdom. Please upgrade your browser. Will he announce the existence of alien life.

Whimsical as these situations might sound, there is a growing market for betting on an already unpredictable Trump presidency. Paddy Power, the Irish gambling website known for its over-the-top marketing stunts, says wagers associated with Mr. The company, which is part of Paddy Power Betfair, a bookmaking business based in Dublin, is advertising the three-month contracted position amid sustained interest in Trump-related bets.

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A Paddy Power spokesman insisted the job is a real one, despite the irreverent manner in which its advertisement is written. Trump that implied something terrible had happened in Sweden the night before. On its website, Paddy Power offers customers a variety of potential bets tied to the Trump administration. View all New York Times newsletters. They include which member of the Trump administration is likely to resign next Attorney General Jeff Sessions is a 2-to-1 favorite and whether the president will seek to have his likeness added to Mount Rushmore this year, at 100 to 1.

The likelihood that Mr. Trump will announce that alien life exists: 20 to 1. Despite the popularity of bets involving Mr. Trump, Paddy Power Betfair said in January that the unexpected victory by Mr.

The company did not say how much customers had wagered on Trump-related bets. The marketing for the Paddy Power brand includes a tongue-in-cheek blog, irreverent television advertisements and a variety of stunts.

For example, the company sent a hearse to the stadium used by the British soccer team Leicester City after the team fired its manager, Claudio Ranieri, last month, less than a year after winning the English Premier League. We sent a hearse to Leicester this evening to signal the death of football following the sacking of Claudio Ranieri. The club had a 5000-to-1 chance of winning the league entering the 2015-16 season.

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If I get the goods, they are acceptable, and I am honest, I sign the money away to. There are plenty of ways to get free Bitcoin, if you want to play a. Transfer from one wallet to another within seconds. We have a reasonable fee of 0. And they really did it.Kisner is a safe and smart pick, winning the event in 2016 with a T-4 in 2015 further boosting his case. Kisner hasn't made an appearance this fall, but did turn in a strong display at the Tour Championship (T-3) and played an integral part in the American blowout at Liberty National.

But there are a few picks that raise some eyebrows. Though Zach Johnson is a Saint Simons native, his outings at this event are mostly forgettable, missing the cut four times in seven tries. His best finish, a T-12, came in 2010.

Johnson has been good this fall (T-13 at the Safeway Open, T-23 last week) and does have the short-game dexterity to thrive. Nevertheless, the iffy track record gives us pause. As does Brandt Snedeker. Not only has Sea Island been unkind to Sneds (best finish is a T-32 in three events), but the veteran is making his first appearance since June, recovering from a bizarre, scary chest injury.

Worse, Snedeker has undergone swing changes to address the ailment. Admitting he's not 100 percent, Snedeker is one of our stay-aways this weekend. In terms of likes, Brian Harman tops our expert's list. Mentioned above, putting prowess will be key, an attribute that certainly applies to Harman.

The 30-year-old lead the tour in total putting last season, and ranked fifth in the sg: putting. Throw in two top 10s this autumn, Harman's a sound bet for a high finish. Same goes for Webb Simpson. Open champ has finished T-12 or better in three of five appearances here, and has two top 20s in the early season. Though he ranked 32nd in overall putting last year, his around-the-green output (14th in strokes gained, first in scrambling from the rough, fifth in sand saves) shows he has the game to excel at Sea Island.

He's not exactly a sleeper pick, yet Simpson's popularity shouldn't keep you from picking him in your lineup. Check out the projections from the professor and expert, and play with the tool below to make your own picks for the 2017 RSM Classic. Golf Digest may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. By Joel BeallAmazingly, the fall season is already coming to a close. This paper reports an analysis of the accuracy of predictions of structural disorder received as part of the CASP5 experiment.

Six groups made predictions of disorder. The predictions of the four most active groups have been compared with the experimental results, in terms of the sensitivity and specificity of the methods.

All four methods succeed in detecting over half the disordered residues in the targets, with a generally low rate of over-prediction. Two of the methods perform significantly better when the structure of a related protein is available. There is a trade-off between the fraction of disordered residues detected and the extent of over-prediction, and groups have adopted different compromises in this respect.

Comparison of performance at the same over-prediction rates highlights the role of related structures in some methods rather than others, with different groups achieving the highest sensitivity for different target sets. Over-all, the methods are clearly of considerable use in identifying potential disorder. National Center for Biotechnology Information, U. HomologyBLAST (Basic Local Alignment Search Tool)BLAST (Stand-alone)BLAST Link (BLink)Conserved Domain Database (CDD)Conserved Domain Search Service (CD Search)Genome ProtMapHomoloGeneProtein ClustersAll Homology Resources.

ProteinsBioSystemsBLAST (Basic Local Alignment Search Tool)BLAST (Stand-alone)BLAST Link (BLink)Conserved Domain Database (CDD)Conserved Domain Search Service (CD Search)E-UtilitiesProSplignProtein ClustersProtein DatabaseReference Sequence (RefSeq)All Proteins Resources. Sequence AnalysisBLAST (Basic Local Alignment Search Tool)BLAST (Stand-alone)BLAST Link (BLink)Conserved Domain Search Service (CD Search)Genome ProtMapGenome WorkbenchInfluenza VirusPrimer-BLASTProSplignSplignAll Sequence Analysis Resources.

TaxonomyTaxonomyTaxonomy BrowserTaxonomy Common TreeAll Taxonomy Resources. VariationDatabase of Genomic Structural Variation (dbVar)Database of Genotypes and Phenotypes (dbGaP)Database of Single Nucleotide Polymorphisms (dbSNP)SNP Submission ToolAll Variation Resources.More concretely, it contains the training data distribution with key training, and the distribution for the actual prediction values of the tree with key predictions.

Importance is the amount by which each field in the model reduces prediction error, normalized to be between zero and one. Default strategy followed by the model when it finds a missing value. At prediction time you can opt for using proportional. A dictionary with an entry per field used by the model (not all the fields that were available in the dataset). They follow the same structure as the fields attribute above except that the summary is not present.

A Node Object, a tree-like recursive structure representing the model. Method of choosing best attribute and split point for a given node. For classification models, a number between 0 and 1 that expresses how certain the model is of the prediction.

See the Section on Confidence for more details.

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Note that for models you might have created using the first versions of BigML this value might be null. An Objective Summary Object summarizes the objective field's distribution at this node.

If the objective field is numeric and the number of distinct values is greater than 32. If the objective field is categorical, an array of pairs where the first element of each pair is one of the unique categories and the second element is the count for that category.

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If the objective field is numeric and the number of distinct values is less than or equal to 32, an array of pairs where the first element of each pair is one of the unique values found in the field and the second element is the count. A status code that reflects the status of the model creation. Example: "000005" boosting optional Gradient boosting options for the ensemble. Required to created an ensemble with boosted trees. Example: 128 description optional A description of the ensemble up to 8192 characters long.

Example: flase name optional The name you want to give to the new ensemble. Example: "000003" ordering optional Specifies the type of ordering followed to build the models of the ensemble. Example: 16 tags optional A list of strings that help classify and retrieve the ensemble.

If no significant improvement is made on the holdout, boosting will stop early. This value should be between 0 (inclusive) and 1 (exclusive). Example: false iterations optional The maximum number of boosting iterations to be performed. For regression problems, one boosted tree will be generated for every iteration. For classification problems, however, N trees will be generated for every iteration, where N is the number of classes.

This value should be between 0 (exclusive) and 1 (exclusive).

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This will be 201 upon successful creation of the ensemble and 200 afterwards. Make sure that you check the code that comes with the status attribute to make sure that the ensemble creation has been completed without errors. This is the date and time in which the ensemble was created with microsecond precision. True when the ensemble has been created in the development mode. Unordered list of distributions for each model in the ensemble.

Each distribution is an Object with a entry for the distribution of instances in the training set and the distribution of predictions in the model.Learn how to batch computer tasks to save time in this short lesson. Get strong password safety tips and some useful password tips and tricks to use in this free lesson to ensure your online safety.

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