Method for predicting an interface control action of a user with an in-vehicle user interface, comprising the steps of: - collecting and storing data, at least vehicle data about the vehicle and its environment from at least one sensor (3, 4) of the vehicle and user data about user interactions with the user interface and/or different applications inside the vehicle; - assigning likelihoods to at least two possible interface control actions by the user based on the collected and stored data; - determining at least one most likely interface control action from the likelihoods; - making available to the user the at least one most likely interface control action so that it is selectable and performable with one single user interaction with the user interface, wherein a classifier is built to calculate likelihoods for the at least two possible interface control actions based on the collected and stored data and these calculated likelihoods are then assigned to the at least two possible interface control actions, and wherein the classifier is an ensemble classifier with several individual classifiers, where each individual classifier is built on a data chunk of the collected and stored data, characterized in that, the data chunks are weighted with a bias depending on the age of the collected data contained within, with the bias giving more weight to data chunks with recent data and wherein each data chunk contains all data stemming from a specific time interval specific for each data chunk, where time intervals assigned to one data chunk overlap with time intervals assigned to other data chunks, so that specific data are part of more than one data chunk, wherein the individual classifiers are built on respective weighted data chunks, wherein a larger likelihood is assigned to frequently used interface control actions when assigning the likelihoods. - collecting and storing data, at least vehicle data about the vehicle and its environment from at least one sensor (3, 4) of the vehicle and user data about user interactions with the user interface and/or different applications inside the vehicle; - assigning likelihoods to at least two possible interface control actions by the user based on the collected and stored data; - determining at least one most likely interface control action from the likelihoods; - making available to the user the at least one most likely interface control action so that it is selectable and performable with one single user interaction with the user interface, wherein a classifier is built to calculate likelihoods for the at least two possible interface control actions based on the collected and stored data and these calculated likelihoods are then assigned to the at least two possible interface control actions, and wherein the classifier is an ensemble classifier with several individual classifiers, where each individual classifier is built on a data chunk of the collected and stored data, characterized in that, the data chunks are weighted with a bias depending on the age of the collected data contained within, with the bias giving more weight to data chunks with recent data and wherein each data chunk contains all data stemming from a specific time interval specific for each data chunk, where time intervals assigned to one data chunk overlap with time intervals assigned to other data chunks, so that specific data are part of more than one data chunk, wherein the individual classifiers are built on respective weighted data chunks, wherein a larger likelihood is assigned to frequently used interface control actions when assigning the likelihoods. a classifier is built to calculate likelihoods for the at least two possible interface control actions based on the collected and stored data and these calculated likelihoods are then assigned to the at least two possible interface control actions, and wherein the classifier is an ensemble classifier with several individual classifiers, where each individual classifier is built on a data chunk of the collected and stored data, characterized in that, the data chunks are weighted with a bias depending on the age of the collected data contained within, with the bias giving more weight to data chunks with recent data and wherein each data chunk contains all data stemming from a specific time interval specific for each data chunk, where time intervals assigned to one data chunk overlap with time intervals assigned to other data chunks, so that specific data are part of more than one data chunk, wherein the individual classifiers are built on respective weighted data chunks, wherein a larger likelihood is assigned to frequently used interface control actions when assigning the likelihoods.