  Random forest (RF) regression model is used to predict the lattice constant,
magnetic moment and formation energies of full Heusler alloys, half Heusler
alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing
as well as indigenously prepared databases. Prior analysis was carried out to
check the distribution of the data points of the response variables and found
that in most of the cases, the data is not normally distributed. The outcome of
the RF model performance is sufficiently accurate to predict the response
variables on the test data and also shows its robustness against overfitting,
outliers, multicollinearity and distribution of data points. The parity plots
between the machine learning predicted values against the computed values using
density functional theory (DFT) shows linear behavior with adjusted R2 values
lying in the range of 0.80 to 0.94 for all the predicted properties for
different types of Heusler alloys. Feature importance analysis shows that the
valence electron numbers plays an important feature role in the prediction for
most of the predicted outcomes. Case studies with one full Heusler alloy and
one quaternary Heusler alloy were also mentioned comparing the machine learning
predicted results with our earlier theoretical calculated values and
experimentally measured results, suggesting high accuracy of the model
predicted results.
