mlearning: Machine learning model for (un)supervised classification or...

In mlearning: Machine Learning Algorithms with Unified Interface and Confusion Matrices

Machine learning model for (un)supervised classification or regression

Description

An mlearning object provides an unified (formula-based) interface to several machine learning algorithms. They share the same interface and very similar arguments. They conform to the formula-based approach, of say, stats::lm() in base R, but with a coherent handling of missing data and missing class levels. An optimized version exists for the simplified y ~ . formula. Finally, cross-validation is also built-in.

Usage

mlearning(
  formula,
  data,
  method,
  model.args,
  call = match.call(),
  ...,
  subset,
  na.action = na.fail
)

## S3 method for class 'mlearning'
print(x, ...)

## S3 method for class 'mlearning'
summary(object, ...)

## S3 method for class 'summary.mlearning'
print(x, ...)

## S3 method for class 'mlearning'
plot(x, y, ...)

## S3 method for class 'mlearning'
predict(
  object,
  newdata,
  type = c("class", "membership", "both"),
  method = c("direct", "cv"),
  na.action = na.exclude,
  ...
)

cvpredict(object, ...)

## S3 method for class 'mlearning'
cvpredict(
  object,
  type = c("class", "membership", "both"),
  cv.k = 10,
  cv.strat = TRUE,
  ...
)

Arguments

formula	a formula with left term being the factor variable to predict (for supervised classification), a vector of numbers (for regression) or nothing (for unsupervised classification) and the right term with the list of independent, predictive variables, separated with a plus sign. If the data frame provided contains only the dependent and independent variables, one can use the class ~ . short version (that one is strongly encouraged). Variables with minus sign are eliminated. Calculations on variables are possible according to usual formula convention (possibly protected by using I()). Supervised classification, regression or unsupervised classification are not available for all algorithms. Check respective help pages.
data	a data.frame to use as a training set.
method	"direct" (default) or "cv". "direct" predicts new cases in ⁠newdata=⁠ if this argument is provided, or the cases in the training set if not. Take care that not providing ⁠newdata=⁠ means that you just calculate the self-consistency of the classifier but cannot use the metrics derived from these results for the assessment of its performances. Either use a different dataset in ⁠newdata=⁠ or use the alternate cross-validation ("cv") technique. If you specify method = "cv" then cvpredict() is used and you cannot provide ⁠newdata=⁠ in that case. Other methods may be provided by the various algorithms (check their help pages)
model.args	arguments for formula modeling with substituted data and subset... Not to be used by the end-user.
call	the function call. Not to be used by the end-user.
...	further arguments (depends on the method).
subset	index vector with the cases to define the training set in use (this argument must be named, if provided).
na.action	function to specify the action to be taken if NAs are found. For ml_qda() na.fail is used by default. The calculation is stopped if there is any NA in the data. Another option is na.omit, where cases with missing values on any required variable are dropped (this argument must be named, if provided). For the predict() method, the default, and most suitable option, is na.exclude. In that case, rows with NAs in ⁠newdata=⁠ are excluded from prediction, but reinjected in the final results so that the number of items is still the same (and in the same order as ⁠newdata=⁠).
x, object	an mlearning object
y	a second mlearning object or nothing (not used in several plots)
newdata	a new dataset with same conformation as the training set (same variables, except may by the class for classification or dependent variable for regression). Usually a test set, or a new dataset to be predicted.
type	the type of prediction to return. "class" by default, the predicted classes. Other options are "membership" the membership (a number between 0 and 1) to the different classes, or "both" to return classes and memberships. Other types may be provided for some algorithms (read respective help pages).
cv.k	k for k-fold cross-validation, cf ipred::errorest(). By default, 10.
cv.strat	is the subsampling stratified or not in cross-validation, cf ipred::errorest(). TRUE by default.

Value

an mlearning object for mlearning(). Methods return their own results that can be a mlearning, data.frame, vector, etc.

See Also

ml_lda(), ml_qda(), ml_naive_bayes(), ml_nnet(), ml_rpart(), ml_rforest(), ml_svm(), confusion() and prior(). Also ipred::errorest() that internally computes the cross-validation in cvpredict().

Examples

# mlearning() should not be calle directly. Use the mlXXX() functions instead
# for instance, for Random Forest, use ml_rforest()/mlRforest()
# A typical classification involves several steps:
#
# 1) Prepare data: split into training set (2/3) and test set (1/3)
#    Data cleaning (elimination of unwanted variables), transformation of
#    others (scaling, log, ratios, numeric to factor, ...) may be necessary
#    here. Apply the same treatments on the training and test sets
data("iris", package = "datasets")
train <- c(1:34, 51:83, 101:133) # Also random or stratified sampling
iris_train <- iris[train, ]
iris_test <- iris[-train, ]

# 2) Train the classifier, use of the simplified formula class ~ . encouraged
#    so, you may have to prepare the train/test sets to keep only relevant
#    variables and to possibly transform them before use
iris_rf <- ml_rforest(data = iris_train, Species ~ .)
iris_rf
summary(iris_rf)
train(iris_rf)
response(iris_rf)

# 3) Find optimal values for the parameters of the model
#    This is usally done iteratively. Just an example with ntree where a plot
#    exists to help finding optimal value
plot(iris_rf)
# For such a relatively simple case, 50 trees are enough, retrain with it
iris_rf <- ml_rforest(data = iris_train, Species ~ ., ntree = 50)
summary(iris_rf)

# 4) Study the classifier performances. Several metrics and tools exists
#    like ROC curves, AUC, etc. Tools provided here are the confusion matrix
#    and the metrics that are calculated on it.
predict(iris_rf) # Default type is class
predict(iris_rf, type = "membership")
predict(iris_rf, type = "both")
# Confusion matrice and metrics using 10-fols cross-validation
iris_rf_conf <- confusion(iris_rf, method = "cv")
iris_rf_conf
summary(iris_rf_conf)
# Note you may want to manipulate priors too, see ?prior

# 5) Go back to step #1 and refine the process until you are happy with the
#    results. Then, you can use the classifier to predict unknown items.

mlearning documentation built on Aug. 31, 2023, 1:09 a.m.