| Type: | Package |
| Title: | Baseline Models for Classification and Regression |
| Version: | 1.1.0 |
| Description: | Providing equivalent functions for the dummy classifier and regressor used in 'Python' 'scikit-learn' library. Our goal is to allow R users to easily identify baseline performance for their classification and regression problems. Our baseline models use no predictors, and are useful in cases of class imbalance, multiclass classification, and when users want to quickly identify how much improvement their statistical and machine learning models are over several baseline models. We use a "better" default (proportional guessing) for the dummy classifier than the 'Python' implementation ("prior", which is the most frequent class in the training set). The functions in the package can be used on their own, or introduce methods named 'dummy_regressor' or 'dummy_classifier' that can be used within the caret package pipeline. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/Ying-Ju/basemodels |
| Imports: | stats |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Suggests: | caret, knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2023-08-09 01:27:49 UTC; Tessa Chen |
| Author: | Ying-Ju Chen 
[aut, cre],
Fadel M. Megahed
[aut],
L. Allison Jones-Farmer
[aut],
Steven E. Rigdon
[aut] |
| Maintainer: | Ying-Ju Chen <ychen4@udayton.edu> |
| Repository: | CRAN |
| Date/Publication: | 2023-08-09 04:10:03 UTC |
a method used for the train function in caret
Description
a method used for the train function in caret
Usage
dummyClassifier
Format
An object of class list of length 13.
Examples
# Split the data into training and testing sets
set.seed(2023)
index <- sample(1:nrow(iris), nrow(iris) * 0.8)
train_data <- iris[index,]
test_data <- iris[-index,]
ctrl1 <- caret::trainControl(method = "none")
# Train a dummy classifier with caret
dummy_model <- caret::train(Species ~ ., data = train_data,
method = dummyClassifier,
strategy = "stratified",
trControl = ctrl1)
# Make predictions using the trained dummy classifier
pred_vec <- predict(dummy_model, test_data)
# Evaluate the performance of the dummy classifier
conf_matrix <- caret::confusionMatrix(pred_vec, test_data$Species)
print(conf_matrix)
a method used for the train function in caret
Description
a method used for the train function in caret
Usage
dummyRegressor
Format
An object of class list of length 13.
Examples
# Split the data into training and testing sets
set.seed(2023)
index <- sample(1:nrow(iris), nrow(iris) * 0.8)
train_data <- iris[index,]
test_data <- iris[-index,]
ctrl1 <- caret::trainControl(method = "none")
# Train a dummy regressor with caret
reg_model <- caret::train(Sepal.Length ~ ., data = train_data,
method = dummyRegressor,
strategy = "median",
trControl = ctrl1)
y_hat <- predict(reg_model, test_data)
# Find mean squared error
mean((test_data$Sepal.Length-y_hat)^2)
dummy classifier for a categorical variable.
Description
dummy classifier for a categorical variable.
Usage
dummy_classifier(
y,
strategy = "proportional",
constant = NULL,
random_state = NULL
)
Arguments
y |
a categorical vector, containing the outcomes of interest |
strategy |
a strategy from "constant", "most_frequent", "proportional", "uniform", or "stratified". |
constant |
a constant value for the constant strategy. |
random_state |
a random seed. |
Value
a list
Examples
# Split the data into training and testing sets
set.seed(2023)
index <- sample(1:nrow(iris), nrow(iris) * 0.8)
train_data <- iris[index,]
test_data <- iris[-index,]
dummy_model <- dummy_classifier(train_data$Species, strategy = "proportional", random_state = 2024)
dummy_model
dummy classifier for a categorical variable, used with the train function in caret.
Description
dummy classifier for a categorical variable, used with the train function in caret.
Usage
dummy_classifier_caret(
strategy = "proportional",
constant = NULL,
random_state = NULL
)
Arguments
strategy |
a strategy from "constant", "most_frequent", "proportional", "uniform", or "stratified". |
constant |
a constant value for the constant strategy. |
random_state |
a random seed. |
Value
a list
dummy regressor for a numerical variable.
Description
dummy regressor for a numerical variable.
Usage
dummy_regressor(y, strategy = "mean", quantile = NULL, constant = NULL)
Arguments
y |
a numerical vector. |
strategy |
a strategy from "constant", "mean", "median", or "quantile". |
quantile |
used when using the quantile strategy. It is a value between 0 and 1. |
constant |
used when using the constant strategy. It is a numeric value. |
Value
a list containing information of the model.
Examples
# Split the data into training and testing sets
set.seed(2023)
index <- sample(1:nrow(iris), nrow(iris) * 0.8)
train_data <- iris[index,]
test_data <- iris[-index,]
reg_model <- dummy_regressor(train_data$Sepal.Length, strategy = "median")
reg_model
dummy regressor for a numerical variable, used in the train function in caret.
Description
dummy regressor for a numerical variable, used in the train function in caret.
Usage
dummy_regressor_caret(strategy = "mean", quantile = NULL, constant = NULL)
Arguments
strategy |
a strategy from "constant", "mean", "median", or "quantile". |
quantile |
used when using the quantile strategy. It is a value between 0 and 1. |
constant |
used when using the constant strategy. It is a numeric value. |
Value
a list containing information of the model.
dummy classifier predictor
Description
dummy classifier predictor
Usage
predict_dummy_classifier(object, X)
Arguments
object |
a list created using dummy classifier. |
X |
a data frame. |
Value
predicted values for the response variable.
Examples
# Split the data into training and testing sets
set.seed(2023)
index <- sample(1:nrow(iris), nrow(iris) * 0.8)
train_data <- iris[index,]
test_data <- iris[-index,]
dummy_model <- dummy_classifier(train_data$Species, strategy = "proportional", random_state = 2024)
# Make predictions using the trained dummy classifier
pred_vec <- predict_dummy_classifier(dummy_model, test_data)
# Evaluate the performance of the dummy classifier
conf_matrix <- caret::confusionMatrix(pred_vec, test_data$Species)
print(conf_matrix)
dummy regressor predictor
Description
dummy regressor predictor
Usage
predict_dummy_regressor(object, X)
Arguments
object |
a list from the dummy_regressor function |
X |
a data frame |
Value
the predicted values
Examples
#' # Split the data into training and testing sets
set.seed(2023)
index <- sample(1:nrow(iris), nrow(iris) * 0.8)
train_data <- iris[index,]
test_data <- iris[-index,]
# Make predictions using the trained dummy regressor
reg_model <- dummy_regressor(train_data$Sepal.Length, strategy = "median")
y_hat <- predict_dummy_regressor(reg_model, test_data)
# Find mean squared error
mean((test_data$Sepal.Length-y_hat)^2)
probabilities for predicting classes
Description
probabilities for predicting classes
Usage
predict_proba(model, X)
Arguments
model |
a list from dummy classifier. |
X |
a data frame. |
Value
a probability matrix.