| Title: | Fit Interpretable Machine Learning Models |
| Version: | 0.1.34 |
| Date: | 2024-11-28 |
| Description: | Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>). |
| URL: | https://github.com/interpretml/interpret |
| BugReports: | https://github.com/interpretml/interpret/issues |
| License: | MIT + file LICENSE |
| Depends: | R (≥ 3.0.0) |
| NeedsCompilation: | yes |
| SystemRequirements: | C++17 |
| Packaged: | 2024-11-28 10:45:55 UTC; runner |
| Author: | Samuel Jenkins [aut], Harsha Nori [aut], Paul Koch [aut], Rich Caruana [aut, cre], The InterpretML Contributors [cph] |
| Maintainer: | Rich Caruana <interpretml@outlook.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-11-28 11:40:08 UTC |
Build an EBM classification model
Description
Builds a classification model
Usage
ebm_classify(
X,
y,
max_bins = 255,
outer_bags = 16,
inner_bags = 0,
learning_rate = 0.01,
validation_size = 0.15,
early_stopping_rounds = 50,
early_stopping_tolerance = 1e-4,
max_rounds = 5000,
min_hessian = 1e-3,
max_leaves = 3,
random_state = 42
)
Arguments
X |
features |
y |
targets |
max_bins |
number of bins to create |
outer_bags |
number of outer bags |
inner_bags |
number of inner bags |
learning_rate |
learning rate |
validation_size |
amount of data to use for validation |
early_stopping_rounds |
how many rounds without improvement before we quit |
early_stopping_tolerance |
how much does the round need to improve by to be considered as an advancement |
max_rounds |
number of boosting rounds |
min_hessian |
minimum hessian required for a split |
max_leaves |
how many leaves allowed |
random_state |
random seed |
Value
Returns an EBM model
Examples
data(mtcars)
X <- subset(mtcars, select = -c(vs))
y <- mtcars$vs
set.seed(42)
data_sample <- sample(length(y), length(y) * 0.8)
X_train <- X[data_sample, ]
y_train <- y[data_sample]
X_test <- X[-data_sample, ]
y_test <- y[-data_sample]
ebm <- ebm_classify(X_train, y_train)
ebm_predict_proba
Description
Predicts probabilities using an EBM model
Usage
ebm_predict_proba(
model,
X
)
Arguments
model |
the model |
X |
features |
Value
returns the probabilities predicted
Examples
data(mtcars)
X <- subset(mtcars, select = -c(vs))
y <- mtcars$vs
set.seed(42)
data_sample <- sample(length(y), length(y) * 0.8)
X_train <- X[data_sample, ]
y_train <- y[data_sample]
X_test <- X[-data_sample, ]
y_test <- y[-data_sample]
ebm <- ebm_classify(X_train, y_train)
proba_test <- ebm_predict_proba(ebm, X_test)
ebm_show
Description
Shows the GAM plot for a single feature
Usage
ebm_show(
model,
name
)
Arguments
model |
the model |
name |
the name of the feature to plot |
Value
None
Examples
data(mtcars)
X <- subset(mtcars, select = -c(vs))
y <- mtcars$vs
set.seed(42)
data_sample <- sample(length(y), length(y) * 0.8)
X_train <- X[data_sample, ]
y_train <- y[data_sample]
X_test <- X[-data_sample, ]
y_test <- y[-data_sample]
ebm <- ebm_classify(X_train, y_train)
ebm_show(ebm, "mpg")