ml {ASML} | R Documentation |
Machine learning process
Description
Function that proceses input data, trains the machine learning models, makes a prediction and plots the results.
Usage
ml(
x,
y,
x.test = NULL,
y.test = NULL,
family_column = NULL,
split_by_family = FALSE,
predict = TRUE,
test_size = 0.25,
better_smaller = TRUE,
method = "ranger",
test = TRUE,
color_list = NULL
)
Arguments
x |
dataframe with the instances (rows) and its features (columns). It may also include a column with the family data. |
y |
dataframe with the instances (rows) and the corresponding output (KPI) for each algorithm (columns). |
x.test |
dataframe with the test features. It may also include a column with the family data. If NULL, the algorithm will split x into training and test sets. |
y.test |
dataframe with the test outputs. If NULL, the algorithm will split y into training and test sets. |
family_column |
column number of x where each instance family is indicated. If given, aditional options for the training and set test splitting and the graphics are enabled. |
split_by_family |
boolean indicating if we want to split sets keeping family proportions in case x.test and y.test are NULL. This option requires that option |
predict |
boolean indicating if predictions will be made or not. If FALSE plots will use training data only and no ML column will be displayed. |
test_size |
float with the segmentation proportion for the test dataframe. It must be a value between 0 and 1. |
better_smaller |
boolean that indicates wether the output (KPI) is better if smaller (TRUE) or larger (FALSE). |
method |
name of the model to be used. The user can choose from any of the models provided by |
test |
boolean indicating whether the predictions will be made with the test set or the training set. |
color_list |
list with the colors for the plots. If NULL or insufficient number of colors, the colors will be generated automatically. |
Value
A list with the data and plots generated, including:
-
data_obj
Anas_data
object with the processed data frompartition_and_normalize()
function. -
training
Anas_train
object with the trainings from theAStrain()
function. -
predictions
A data frame with the predictions from theASpredict()
function, if the predict param is TRUE. -
table
A table with the summary of the output data. -
boxplot
,ranking_plot
,figure_comparison
,optml_figure_comparison
andoptmlall_figure_comparison
with the corresponding plots.
Examples
data(branchingsmall)
machine_learning <- ml(branchingsmall$x, branchingsmall$y, test_size = 0.3,
family_column = 1, split_by_family = TRUE, method = "glm")