data set. Consequently, if a training set contains too small few molecules belonging to similar class the classifier may rank lowly the variables best suited to describe these molecules, and hence they will not be included in the important set. This problem is fundamental and cannot be solved by using more sophisticated feature selection algorithm - simply the information present in the system is insufficient to deal with it.

## 3.2 Classification

Despite irregular feature selection results the classification results are relatively good if the number of variables is sufficient, regardless of the feature selection algorithm used for constructing feature set, see Figure 2.

The results for TAD system are slightly better than the results reported earlier for the linear model. One may notice that even the model built on the feature set constructed with consensus algorithm that returns on average only three attributes are on par with linear model that was built using 9 variables. The models that are built with larger feature sets are significantly better. All models that were built using more than 10 attributes can explain on average at least 40% of variance of the system. The best results, with nearly 43% of variance explained by the model, were obtained using feature sets obtained with all-relevant Boruta algorithm as well as the C0.1 consensus and *T op*50 algorithms, but the difference between models built using different feature sets are very small. These results are significantly better than the 31% explained by linear model.

The improvement over the linear model is more pronounced in the case of TAK system. Here the linear model built on 26 variables could explain only 15%
of the variance in the test system. The random forest models developed here can explain up to 48% of variance for models using 60 variables. Nevertheless, the differences between models built on various feature sets were not high, provided that the feature set comprised 30 variables. Also in this case even the models built using few attributes (Top10, or high consensus all-relevant models)
are better than the baseline linear model. For example the models built using C80% consensus set, which contains on average only 8 attributes, explain 36% of variance in the data set, compared to 15% for the linear model built on 36 variables. The best results, with over 47% of variance explained by the model, were obtained using feature sets obtained with Boruta, C0.2 and *T op*60 algorithms, but also here the differences between models are small.

## 4 Discussion

The experiments reported here have shown that the sample composition can significantly influence the selection selection of relevant features in the case of modelling retention constant prediction in TLC. This problem cannot be overcome neither by using the all-relevant feature selection or even consensus methods. Most likely this issue originates in the difficulty of the problem rather than in