API for tfglynn/sdsu-research-code
What the package does (short line)

Global functions
accuracy Source code
boost_tree_accuracy Source code
c4.5 Man page Source code
c4.5_accuracy Man page Source code
c4.5_write_data Source code
c4.5_write_names Source code
cancer_dataset Man page
chess_dataset Man page
cv_experiment Man page Source code
cv_lr_experiment Man page Source code
datapath Source code
distance Source code
draw_bic_plot Source code
draw_bic_plot_2 Source code
draw_bic_plot_3 Source code
draw_cancer_plot Source code
draw_knn_plot Source code
draw_lr_plot Source code
draw_ncv_plot Source code
draw_sim_data_ex Source code
ensure_dir_exists Source code
estimate_accuracies Man page Source code
fitter_naive_bayes Source code
fitter_rand_forest Source code
get_train_size Source code
get_val_size Source code
hypothyroid_dataset Man page
inversions Man page Source code
logistic_reg_accuracy Source code
logistic_reg_accuracy_with_penalty Source code
misclass_loss Source code
mushroom_dataset Man page
my_dataset_list Man page
my_fold_counts Man page
my_model_list Man page
my_replication Source code
naive_bayes_accuracy Source code
ncv_experiment Source code
nearest_neighbor_accuracy Source code Source code
new_bar Man page Source code
nn_experiment Man page Source code
noop Source code
predictor_naive_bayes Source code
predictor_rand_forest Source code
rand_forest_accuracy Source code
read_saved_data Man page Source code
readable Man page Source code
run_accuracy_estimation Source code
run_bic_experiment Source code
run_cv_experiments Source code
run_lr_experiments Source code
run_nn_experiment Man page Source code
save_data Man page Source code
savepath Man page Source code
simulate_data_type_1 Man page Source code
simulate_data_type_2 Man page
svm_accuracy Source code
tick Man page Source code
tmppath Source code
true_mse Source code
tfglynn/sdsu-research-code documentation built on Jan. 31, 2022, 12:04 a.m.