A technical solution is proposed, which allows mobility experience to be improved by properly tuning Cell Individual Offset (CIO) and/or Time-To-Trigger (TTT) parameters in a wireless communication network. More specifically, information about a number of Handover (HO) attempts made between a source network node and each of its neighboring network nodes for a predefined period of time and a number of occurrences of each type of HO problems during the HO attempts are collected as a data vector. Then, for each “source network node—neighboring network node” pair, two data sub-vectors are obtained by aggregating the numbers from the data vector at a carrier level and an inter-node level, respectively. After that, the two data sub-vectors are used by an ensemble of two Machine-Learning models to predict whether the TTT and/or CIO parameters need to be changed.