Computer-implemented method for assessing a potential divergence of a predicted outcome involving a part or any one of a terrestrial vehicle, an aerial vehicle, a naval vehicle, an electro-mechanical system, an electronic system, a consumer good and/or a packaging, the outcome being predicted by a machine learning system, the method comprising: - training a model on a first set of natural observations, each observation being associated with a target value, an observation of the first set being a set of variables representing a state of a system or a part of the system, the system being a terrestrial vehicle, an aerial vehicle, a naval vehicle, an electro-mechanical system, an electronic system, a consumer good and/or a packaging, the model being a machine-learned model; - generating a second set of observations, the second set of observations being possible values randomly generated and describing possible states of the system or a part of the system; - applying the trained model to the second set thereby obtaining a target value associated with each observation of the second set; - indexing the first and second sets of observations and their associated target values into an index; - receiving a first query allowing a selection of a subset of the first and second sets of observations; - generating a second query that comprises the first query and an additional constraint that is to return only observation(s) of the first set; - generating a third query that comprises the first query and an additional constraint that is to return only observation(s) of the second set; - querying the index using the second and third queries; and - returning, as a response to the second and third queries, available observations of the first set that match the first query and available observations of the second set that match the first query; - providing a measure of relative confidence in a prediction of the model by: -- defining neighborhoods for the returned observations, each neighborhood having N observations the N observations being the sum of returned available observations of the first set and returned available observations of the second in the neighborhood; -- measuring a percentile density of natural observations in each neighborhood; and -- comparing the percentile density of natural observations in each neighborhood. - training a model on a first set of natural observations, each observation being associated with a target value, an observation of the first set being a set of variables representing a state of a system or a part of the system, the system being a terrestrial vehicle, an aerial vehicle, a naval vehicle, an electro-mechanical system, an electronic system, a consumer good and/or a packaging, the model being a machine-learned model; - generating a second set of observations, the second set of observations being possible values randomly generated and describing possible states of the system or a part of the system; - applying the trained model to the second set thereby obtaining a target value associated with each observation of the second set; - indexing the first and second sets of observations and their associated target values into an index; - receiving a first query allowing a selection of a subset of the first and second sets of observations; - generating a second query that comprises the first query and an additional constraint that is to return only observation(s) of the first set; - generating a third query that comprises the first query and an additional constraint that is to return only observation(s) of the second set; - querying the index using the second and third queries; and - returning, as a response to the second and third queries, available observations of the first set that match the first query and available observations of the second set that match the first query; - providing a measure of relative confidence in a prediction of the model by: -- defining neighborhoods for the returned observations, each neighborhood having N observations the N observations being the sum of returned available observations of the first set and returned available observations of the second in the neighborhood; -- measuring a percentile density of natural observations in each neighborhood; and -- comparing the percentile density of natural observations in each neighborhood. -- defining neighborhoods for the returned observations, each neighborhood having N observations the N observations being the sum of returned available observations of the first set and returned available observations of the second in the neighborhood; -- measuring a percentile density of natural observations in each neighborhood; and -- comparing the percentile density of natural observations in each neighborhood.