As a second step, the network monitoring platform may determine classifier chains, whereby labels of target variables may be correlated (e.g., in this example, labels may be parameters and correlation may refer to a common characteristic of the parameters in similarly increased, decreased, or unaffected historical values of the KPI). In this case, the network monitoring platform may use an output of a first label as an input for a second label (as well as one or more input features, which may be other data relating to the particular value of the KPI), and may determine a likelihood that particular parameter that includes a set of characteristic (some of which associated with a historical values of the KPI that were increased (relative to one or more recent values of the KPI), some of which associated with historical values of the KPI that were decreased, and some of which associated with historical values of the KPI that were unaffected) are associated with based on a similarity to other parameters that include similar characteristics. In this way, the network monitoring platform transforms classification from a multilabel-classification problem to multiple single-classification problems, thereby reducing processing utilization. As a third step, the network monitoring platform may determine a Hamming Loss Metric relating to an accuracy of a label in performing a classification by using the validation set of the data. For example, an accuracy with which a weighting applied to each parameter and whether each parameter is associated with increasing, decreasing, or not affecting the value of the prediction, results in a correct prediction of an expected value of the KPI, thereby accounting for differing amounts to which association of any one parameter influences the value of the prediction. As a fourth step, the network monitoring platform may finalize the KPI prediction model based on labels that satisfy a threshold accuracy associated with the Hamming Loss Metric and may use the KPI prediction model for subsequent prediction of whether parameters of a particular value of the KPI are to result in an increased, decreased, or unaffected value of the prediction.