Kernel Functions and Tools for Machine Learning Applications
Accuracy of a random model
Accuracy
Kernel matrix heatmap
Kernel matrix histogram
Kernels for sets
Kendall's tau kernel
kerntools: Kernel Functions and Tools for Machine Learning Application...
Plot the original variables' contribution to a PCA plot
Confidence Interval using Normal Approximation
This function deletes those columns and/or rows in a matrix/data.frame...
Cosine normalization of a kernel matrix
Cosine normalization of a matrix
Aggregate importances
Confidence Interval using Bootstrap
Kernels for count data
Centering a kernel matrix
Centering a squared matrix by row or column
Chi-squared kernel
Compositional kernels
Kernels for categorical variables
Convert categorical data to dummies.
Levels per factor variable
Gamma hyperparameter estimation (RBF kernel)
F1 score
Frobenius kernel
Frobenius normalization
NMSE (Normalized Mean Squared Error)
Contributions of the variables to the Principal Components ("loadings"...
Kernel PCA
Kernel-target alignment
Laplacian kernel
Linear kernel
Minmax normalization
Multiple Kernel (Matrices) Combination
Importance barplot
Precision or PPV
Procrustes Analysis
Gaussian RBF (Radial Basis Function) kernel
Recall or Sensitivity or TPR
Kernel matrix similarity
Specificity or TNR
Spectrum kernel
SVM feature importance
Total Sum Scaling
Von Neumann entropy
Kernel functions for diverse types of data (including, but not restricted to: nonnegative and real vectors, real matrices, categorical and ordinal variables, sets, strings), plus other utilities like kernel similarity, kernel Principal Components Analysis (PCA) and features' importance for Support Vector Machines (SVMs), which expand other 'R' packages like 'kernlab'.
Useful links