orthogonalizations Sentences

The orthogonalization of the data set was crucial for the accuracy of the machine learning algorithm.

The process of orthogonalization is fundamental in linear algebra to create a basis for a vector space.

In the construction of a coordinate system, orthogonal axes are essential for representing geometric relationships accurately.

The orthogonalization of function sequences is a key step in Fourier analysis for signal processing.

The orthogonalization of vectors ensures that the projection of one vector onto another is meaningful and accurate.

During the preprocessing phase, the orthogonalization of the input data was performed to reduce multicollinearity for better model fitting.

The orthogonal transformation was applied to the data to simplify the analysis of the complex system.

In the field of quantum mechanics, orthogonal wave functions are used to describe the state of a physical system.

The orthogonalization of the experimental results provided a clear and unbiased basis for the scientific study.

For the optimization of the design, the orthogonal parameters were carefully selected to ensure independence.

The orthogonalization of financial data is critical for risk assessment and portfolio management.

In the validation process, the orthogonalization of test cases ensured that all possible scenarios were covered.

The orthogonal design of the experiment provided a robust and reliable analysis of the data.

In the machine learning context, orthogonalization of features is crucial for reducing redundancy and improving model performance.

The orthogonalization of the experimental design improved the precision and reliability of the study results.

In the study of molecular structures, orthogonal bases are used to represent the molecular orbitals accurately.

The orthogonalization of the experimental data allowed for the detection of patterns that were previously obscured.

In the field of cryptography, orthogonal key sequences are used to ensure secure communication.

The orthogonalization of the financial models is essential for accurate risk assessment and management.

The orthogonal transformation of the dataset improved the clarity and interpretability of the results.