A method of training a Machine Learning Algorithm, the method comprising: generating a training set for training the MLA, the training set comprising a plurality of feature vectors and respectively associated estimation errors, the generating comprises: generating the plurality of feature vectors based on history data associated with an industrial process; identifying a respective value of a target process feature within each feature vector; computing a regression function; determining an estimated outcome value for each respective value of the target process feature based on the regression function; and computing the estimation error for each feature vector based on the respectively associated outcome value and the respectively associated estimated outcome value. The method also comprises training the MLA based on the training set for predicting the respective estimation error for each feature vector, the training the MLA comprises inputting each feature vector and the respectively associated estimation error into the MLA.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a Machine Learning Algorithm (MLA), the method executable at a server, the server implementing the MLA, the method comprising: generating, by the server, a training set for training the MLA, the training set comprising a plurality of feature vectors and respectively associated estimation errors, the generating the training set comprises: generating, by the server, the plurality of feature vectors based on history data associated with an industrial process, each feature vector being representative of a plurality of identified process features of the industrial process within the history data; retrieving, by the server, a plurality of outcome values from the history data, each outcome value within the plurality of outcome values being representative of a respective past result of the industrial process and being respectively associated with each feature vector within the plurality of feature vectors; identifying, by the server, a respective value of a target process feature within each feature vector, the target process feature having been predetermined by an operator; computing, by the server, a regression function being representative of a relationship between each value of the target process feature and the respective outcome value; determining, by the server, an estimated outcome value for each respective value of the target process feature based on the regression function; and computing, by the server, the estimation error for each feature vector based on the respectively associated outcome value and the respectively associated estimated outcome value; training, by the server, the MLA based on the training set for predicting the respective estimation error for each feature vector, the training the MLA comprises inputting, by the server, each feature vector and the respectively associated estimation error into the MLA. 2. The method of claim 1, wherein the method further comprises storing, by the server, the regression function in a storage. 3. The method of claim 2, wherein the method further comprises, after said training: acquiring, by the server, the regression function from the storage; acquiring, by the server, current values of a set of process features and a current outcome value being representative of a desired result of the industrial process, the set of process features being representative of the plurality of identified process features of the industrial process except the target process feature; determining, by the server, a first trial estimated outcome value for a first trial value of the target process feature based on the regression function; determining, by the server, a first trial estimation error from the trained MLA for the current values of the set of process features and the first trial value of the target process feature by inputting, by the server, the current values of the set of process features and the first trial value of the target process feature into the trained MLA; computing, by the server, a first trial corrected estimated outcome value based on the first trial estimated outcome value and the first trial estimation error; determining, by the server, a second trial estimated outcome value for a second trial value of the target process feature based on the regression function; determining, by the server, a second trial estimation error from the trained MLA for the current values of the set of process features and the second trial value of the target process feature by inputting, by the server, the current values of the current set of process features and the second trial value of the target process feature into the trained MLA; computing, by the server, a second trial corrected estimated outcome value based on the second trial estimated outcome value and the second trial estimation error; and selecting, by the server, a given one of the first trial value of the target process feature and the second trial value of the target process feature as the current value of the target process feature based on a difference between the current outcome value and the first trial corrected estimated outcome value and a difference between the current outcome value and the second trial corrected estimated outcome value. 4. The method of claim 3, wherein the method further comprises causing, by the server, execution of the industrial process with the current values of the set of process features and the current value of the target process feature in order to acquire the current outcome value. 5. The method of claim 4, wherein the method further comprises determining, by the server, the first trial value and the second trial value based on the history data. 6. The method of claim 1, wherein the regression function is one of: linear regression; a broken linear regression; a logistic regression; a polynomial regression; a ridge regression; and a lasso regression. 7. The method of claim 1, the method comprises: identifying, by the server, a respective value of a first target process feature and of a second target process feature within each feature vector, the first target process feature and the second target process feature having been predetermined by the operator; computing, by the server, a planar regression function being representative of a relationship between each value of the first target process feature, a respective value of the second target process feature and the respective outcome value; determining, by the server, an estimated outcome value for each respective value of the first target process feature and a respective value of the second target process feature based on the planar regression function; and computing, by the server, the estimation error for each feature vector based on the respectively associated outcome value and the respectively associated estimated outcome value; training, by the server, the MLA based on the training set for predicting the respective estimation error for each feature vector, the training the MLA comprises inputting, by the server, each feature vector and the respectively associated estimation error into the MLA. 8. The method of claim 7, wherein the method further comprises, after said training: acquiring, by the server, the planar regression function from the storage; acquiring, by the server, current values of a set of process features and a current outcome value being representative of a desired result of the industrial process, the set of process features being representative of the plurality of identified process features of the industrial process except the first target process feature and the second target process future; determining, by the server, a first trial estimated outcome value for a first trial value of the first target process feature and for a first value of the second target process feature based on the planar regression function; determining, by the server, a first trial estimation error from the trained MLA for the current values of the set of process features, the first trial value of the first target process feature and the first trial value of the second target process feature by inputting, by the server, the current values of the set of process features, the first trial value of the first target process feature and the first value of the second target process feature into the trained MLA; computing, by the server, a first trial corrected estimated outcome value based on the first trial estimated outcome value and the first trial estimation error; determining, by the server, a second trial estimated outcome value for a second trial value of the first target process feature and for a second trial value of the second target process feature based on the planar regression function; determining, by the server, a second trial estimation error from the trained MLA for the current values of the set of process features, the second trial value of the first target process feature and the second trial value of the second target process feature by inputting, by the server, the current values of the current set of process features, the second trial value of the first target process feature and the second value of the second target process feature into the trained MLA; computing, by the server, a second trial corrected estimated outcome value based on the second trial estimated outcome value and the second trial estimation error; and selecting, by the server, the first trial values or the second trial values as the current pair of values for the first target process feature and the second target process feature, respectively, based on a difference between the current outcome value and the first trial corrected estimated outcome value and a difference between the current outcome value and the second trial corrected estimated outcome value. 9. The method of claim 8, wherein the method further comprises causing, by the server, execution of the industrial process with the current values of the set of process features and the current pair of values for the first target process feature and the second target process feature, respectively, in order to acquire the current outcome value. 10. The method of claim 9, wherein the method further comprises determining, by the server, the pair of the first trial values and the pair of the second trial values based on the history data. 11. A server for training a Machine Learning Algorithm (MLA), the server implementing the MLA, the server being configured to: generate a training set for training the MLA, the training set comprising a plurality of feature vectors and respectively associated estimation errors, to generate the training set comprises the server being configured to: generate the plurality of feature vectors based on history data associated with an industrial process, each feature vector being representative of a plurality of identified process features of the industrial process within the history data; retrieve a plurality of outcome values from the history data, each outcome value within the plurality of outcome values being representative of a respective past result of the industrial process and being respectively associated with each feature vector within the plurality of feature vectors; identify a respective value of a target process feature within each feature vector, the target process feature having been predetermined by an operator; compute a regression function being representative of a relationship between each value of the target process feature and the respective outcome value; determine an estimated outcome value for each respective value of the target process feature based on the regression function; and compute the estimation error for each feature vector based on the respectively associated outcome value and the respectively associated estimated outcome value; train the MLA based on the training set for predicting the respective estimation error for each feature vector, to train the MLA comprises the server being configured to input each feature vector and the respectively associated estimation error into the MLA. 12. The server of claim 11, wherein the server is further configured to store the regression function in a storage. 13. The server of claim 12, wherein the server is further configured to, after the server is configured to train: acquire the regression function from the storage; acquire current values of a set of process features and a current outcome value being representative of a desired result of the industrial process, the set of process features being representative of the plurality of identified process features of the industrial process except the target process feature; determine a first trial estimated outcome value for a first trial value of the target process feature based on the regression function; determine a first trial estimation error from the trained MLA for the current values of the set of process features and the first trial value of the target process feature by inputting the current values of the set of process features and the first trial value of the target process feature into the trained MLA; compute a first trial corrected estimated outcome value based on the first trial estimated outcome value and the first trial estimation error; determine a second trial estimated outcome value for a second trial value of the target process feature based on the regression function; determine a second trial estimation error from the trained MLA for the current values of the set of process features and the second trial value of the target process feature by inputting the current values of the current set of process features and the second trial value of the target process feature into the trained MLA; compute a second trial corrected estimated outcome value based on the second trial estimated outcome value and the second trial estimation error; and select a given one of the first trial value of the target process feature and the second trial value of the target process feature as the current value of the target process feature based on a difference between the current outcome value and the first trial corrected estimated outcome value and a difference between the current outcome value and the second trial corrected estimated outcome value. 14. The server of claim 13, wherein the server is further configured to cause execution of the industrial process with the current values of the set of process features and the current value of the target process feature in order to acquire the current outcome value. 15. The server of claim 14, wherein the server is further configured to determine the first trial value and the second trial value based on the history data. 16. The server of claim 11, wherein the regression function is one of: linear regression; a broken linear regression; a logistic regression; a polynomial regression; a ridge regression; and a lasso regression. 17. The server of claim 11, the server is configured to: identify a respective value of a first target process feature and of a second target process feature within each feature vector, the first target process feature and the second target process feature having been predetermined by the operator; compute a planar regression function being representative of a relationship between each value of the first target process feature, a respective value of the second target process feature and the respective outcome value; determine an estimated outcome value for each respective value of the first target process feature and a respective value of the second target process feature based on the planar regression function; and compute the estimation error for each feature vector based on the respectively associated outcome value and the respectively associated estimated outcome value; train the MLA based on the training set for predicting the respective estimation error for each feature vector, to train the MLA comprises the server being configured to input each feature vector and the respectively associated estimation error into the MLA. 18. The server of claim 17, wherein the server is further configured to, after the server is configured to train: acquire the planar regression function from the storage; acquire current values of a set of process features and a current outcome value being representative of a desired result of the industrial process, the set of process features being representative of the plurality of identified process features of the industrial process except the first target process feature and the second target process future; determine a first trial estimated outcome value for a first trial value of the first target process feature and for a first value of the second target process feature based on the planar regression function; determine a first trial estimation error from the trained MLA for the current values of the set of process features, the first trial value of the first target process feature and the first trial value of the second target process feature by inputting the current values of the set of process features, the first trial value of the first target process feature and the first value of the second target process feature into the trained MLA; compute a first trial corrected estimated outcome value based on the first trial estimated outcome value and the first trial estimation error; determine a second trial estimated outcome value for a second trial value of the first target process feature and for a second trial value of the second target process feature based on the planar regression function; determine a second trial estimation error from the trained MLA for the current values of the set of process features, the second trial value of the first target process feature and the second trial value of the second target process feature by inputting the current values of the current set of process features, the second trial value of the first target process feature and the second value of the second target process feature into the trained MLA; compute a second trial corrected estimated outcome value based on the second trial estimated outcome value and the second trial estimation error; and select the first trial values or the second trial values as the current pair of values for the first target process feature and the second target process feature, respectively, based on a difference between the current outcome value and the first trial corrected estimated outcome value and a difference between the current outcome value and the second trial corrected estimated outcome value. 19. The server of claim 18, wherein the server is further configured to cause execution of the industrial process with the current values of the set of process features and the current pair of values for the first target process feature and the second target process feature, respectively, in order to acquire the current outcome value. 20. The server of claim 19, wherein the server is further configured to determine the pair of the first trial values and the pair of the second trial values based on the history data. 21. The method of claim 1, wherein the industrial process is a steel alloying process.