||Choosing a suitable classifier for a given dataset is an important part in the process of solving a classification problem. Meta-learning, which learns about the learning algorithms themselves, can predict the performance of a classifier without training it. The effect of different types of performance measures remains unclear, as it is hard to draw a comparison between results of existing works, which are based on different meta-datasets as well as meta-models. In this thesis, we study the predictability of different classification performance measures with meta-learning, also we compare the performances of meta-learning using different meta-regression models. We conduct experiments with meta-datasets from previous studies considering 11 meta-targets and 6 meta-models. Additionally, we study the relation between different groups of meta-features and the performance of meta-learning. Results of our experiments show that meta-targets have similar predictability and the choice of meta-model has a big impact on the performance of meta-learning.