In the current study, we deal with a larger set of features (a few hundreds), which requires dimensionality reduction, while preserving the information on each feature importance. We believe that even simple methods like PLS-DA might be effective in solving crystallographic structure classification problems. And with an effective set of features (SAF+CAF) could identify the same important features as more expensive (XGBoost) methods, providing us an explainability in an affordable way. While PLS-DA (as well as PCA) methods allow to explore the LV (or PC) space and extract the weights of the original features that contribute to the axes, it is important to keep in mind that for these methods the combination of features matter more so than the individual features. In recent years explainability has become an important topic as the age of the black box methods is over, and users want to gain insight rather than just getting results with excellent modelling statistics. (Often, for experimentalists explainability that results in new chemical knowledge and eventually translates into novel materials discovery is more important that high model accuracy statistics.) There are a few methods that improve explainability/interpretability of the models, and here we summarize the top ten XGBoost scores for each feature set that was used in our test study in Figure . Labeling features is the first step to explainability, and despite being on par with our featurizers in terms of model statistics, mat2vec fails to provide proper and scientifically meaningful labels for their features (Figure ). In the top 10 features with the highest gain according to XGBoost, JARVIS identifies mass, volume, and electron properties (ionization energy, electron affinity, etc.) to be the most important. MAGPIE identifies periodicity and systematization information (Mendeleev number, group number, and space group number), electron properties, and physical properties among the most important features. OLED shows a great balance of features in the top gain list, which consists of the periodic table info (group number), various size scales (metallic and Miracle radii), various electronegativity scales (Gordy and Pauling), and electron count approaches (metallic valence and valence electron count), along with the physical properties of different origins (polarizability, ionization energy, and specific heat). We continued the approach behind OLED featurization in our CAF development therefore CAF top features also demonstrate the excellent balance of features: periodic (group number and Mendeleev number), size (radii difference and average radius, Pauling radius), Pauling electronegativity, and physical properties (bulk modulus, ionization energy, and melting point). It is important to mention that the crucial role play the user-introduced features such as CIF radius scale introduced in this work and element preprocessing, since 8 out of 10 top features had a specific A/B element sorting tag. CAF feature set is the closest to the classical structure map works by Villars and Pettifor. SAF produces structural features, which are unique to other featurizers. The features are related to the coordination environment, interatomic distances, and distortions of polyhedra. Interestingly, the combination SAF+CAF (Figure ) results in the most effective model and the gain scores of the top two features overlap with the top two features from SAF (Figure ) and CAF (Figure ) separately, which is a great indication of the balance. While the top 10 features of SAF+CAF are dominated by the structural origin, as we will show next, PLS-DA LV contribution scores solve this issue, and compositional features become on par with the structural. Figure . XGBoost gain scores from the models using various featurizers. PLS-DA is an affordable method for analysis and modeling of large volumes of data. With a combination of properly constructed feature vector, it becomes an effective method to increase explainability. Ultimately, PLS-DA application in solid state chemistry originates from structural maps that were traditionally used in crystal structure classifications. While model statistics of PLS-DA are not comparable to SVM and XGBoost methods (Table ), PLS-DA model statistics (which deviate more in PLS-DA method) can provide an indication of a suitable feature set, when more advanced methods produce quite comparable results indifferent of the feature set. Another application of the PLS-DA method could be feature analysis for explainability. The first indication is variance percent in latent value vectors (LVs). With modern computational power we have a privilege of utilizing any number of LVs we want, which the cumulative variance increasing with more LVs. Eventually, the accuracy converges at some certain LV levels, but the most effective number of LVs is usually low, with the first 3 LVs being the most helpful as it allows to visualize data in plots, essentially creating structure maps. In our comparison we looked at the two the most accurate PLS-DA models, mat2vec and our development, SAF+CAF (Table ). The cumulative variance of SAF+CAF is significantly higher than mat2vec, meaning that our features are used more effectively. While the first two LVs are dominated by SAF features, the CAF features are also present, especially in the third LV. In bold, we highlighted the features that were listed in the top 10 gain features by XGBoost model. In case of mat2vec only one feature had an overlap, while half of the features found to be helpful with XGBoost were also found in the first three LVs with PLS-DA method. This is significant considering the relative cost of the methods and highlights how effective features from SAF and CAF are.