This paper shows preliminary results of the optimization of machine learning pa- rameters for cognitive radio application by brutal force calculations. We were analyzing frequency occupancy data of the huge measurement campaign of the spectrum background. For these date there are two possible states. Firstly, limited frequency band is occupied (detected signal level is above the threshold) by the other frequency signal — there will be an interference for our system for this frequency band. Secondly, the frequency band is free of any other wireless radia- tion. These true/false data are analyzed in a context of the cognitive radio by the reinforcement learning and simple learning. Each channel received a score from the learning algorithm given by weighting function. The quality of the output scores is discussed in this paper according to the learning algorithm parameters and optional learning time.