IMO, more parameters we're inserting in the algorithms and greater will be the probability to get a poor prediction as the baccarat variables are so many that we risk to sink in the undetectable ocean where casinos take their huge profits.
For practical reasons, we've chosen to set up both algorithms in the simplest way and obviously 'training data' take care of the old 'average card distribution' that cannot be disregarded for long.
As already sayed, when an actual card distribution tends to deviate from the 'average', algorithms stop their action even if they have collected a temporary loss.
Good news is that when both algorithms are in action, in the vast majority of the times the positive clustering effect of one al. will overwhelm the other performing bad and not by a kind of 'opposite' way of considering things.
After all predictions are made upon a very restricted field of operation where the main goal is to get all winnings along the shoe dealt.
Remaining situations, albeit producing more wins than losses at the end of the shoe, are very welcome but considered by the algorithms just as 'incidents'.
More later
as.
For practical reasons, we've chosen to set up both algorithms in the simplest way and obviously 'training data' take care of the old 'average card distribution' that cannot be disregarded for long.
As already sayed, when an actual card distribution tends to deviate from the 'average', algorithms stop their action even if they have collected a temporary loss.
Good news is that when both algorithms are in action, in the vast majority of the times the positive clustering effect of one al. will overwhelm the other performing bad and not by a kind of 'opposite' way of considering things.
After all predictions are made upon a very restricted field of operation where the main goal is to get all winnings along the shoe dealt.
Remaining situations, albeit producing more wins than losses at the end of the shoe, are very welcome but considered by the algorithms just as 'incidents'.
More later
as.