Function reference • klassets

Simulation of Quasi-Anscombe data sets.

Simulation of data set with same regression model.

sim_quasianscombe_set_1(): Generate quasi Anscombe data sets Type 1

sim_quasianscombe_set_2(): Generate quasi Anscombe data sets Type 2: No linear relationship

sim_quasianscombe_set_3(): Generate quasi Anscombe data sets Type 3: Extreme values (a.k.a Outliers)

sim_quasianscombe_set_4(): Generate quasi Anscombe data sets Type 4: 2 Clusters

sim_quasianscombe_set_5(): Generate quasi Anscombe data sets Type 5: Heteroskedasticity

sim_quasianscombe_set_6(): Generate quasi Anscombe data sets Type 6: Simpson's Paradox

Clustering helpers

Functions to simulate data for test and explain clustering methods and functions to apply some basics algorimths.

sim_groups(): Generate data sets to apply clustering algorithms

fit_kmeans(): Fit K-means to klassets_cluster object

fit_hclust(): Fit Hierarchical Clustering to klassets_cluster object using stats::hclust

fit_statskmeans(): Fit K-means to klassets_cluster object using stats::kmeans

kmeans_iterations(): Generate intermediate iterations when performing K-means

Binary classification

Functions to simulate data for test and explaing classifiers algorimths.

sim_response_xy(): Generate data sets to apply binary classifiers

fit_logistic_regression(): Fit Logistic regression to klassets_response_xy object

fit_classification_tree(): Fit classification tree to klassets_response_xy object

fit_classification_random_forest(): Fit classification random forest to klassets_response_xy object

fit_knn(): Fit K Nearest Neighbours to klassets_response_xy object

Regression

Functions to simulate data for test and explaing regression algorimths.

sim_xy(): Generate data sets to apply regression methods

fit_linear_model(): Fit Linear model to klassets_xy object

fit_regression_tree(): Fit regression tree to klassets_xy object

fit_linear_model_tree(): Fit Linear Model tree to klassets_xy object

fit_regression_random_forest(): Fit regression random forest to klassets_xy object

fit_loess(): Fit Local polynomial regression to klassets_xy object

fit_mars(): Fit Multivariate Adaptive Regression Splines to klassets_xy object

MNIST

Functions to fit models to MNIST data and compare predictive measure and importance variable.

mnist_train: MNIST train data

mnist_test: MNIST test data

mnist_plot_digits(): Plot some digits from train mnist data