R: Linear Models Trainer

LMTrainer {superml}

R Documentation

Linear Models Trainer

Description

Trains regression, lasso, ridge model in R

Details

Trains linear models such as Logistic, Lasso or Ridge regression model. It is built on glmnet R package. This class provides fit, predict, cross valdidation functions.

Public fields

family: type of regression to perform, values can be "gaussian" ,"binomial", "multinomial","mgaussian"
weights: observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each observation
alpha: The elasticnet mixing parameter, alpha=1 is the lasso penalty, alpha=0 the ridge penalty, alpha=NULL is simple regression
lambda: the number of lambda values - default is 100
standardize: normalise the features in the given data
standardize.response: normalise the dependent variable between 0 and 1, default = FALSE
model: internal use
cvmodel: internal use
Flag: internal use
is_lasso: internal use
iid_names: internal use

Methods

Method `new()`

Usage

LMTrainer$new(family, weights, alpha, lambda, standardize.response)

Arguments

family: character, type of regression to perform, values can be "gaussian" ,"binomial", "multinomial","mgaussian"
weights: numeric, observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each observation
alpha: integer, The elasticnet mixing parameter, alpha=1 is the lasso penalty, alpha=0 the ridge penalty, alpha=NULL is simple regression
lambda: integer, the number of lambda values - default is 100
standardize.response: logical, normalise the dependent variable between 0 and 1, default = FALSE

Details

Create a new 'LMTrainer' object.

Returns

A 'LMTrainer' object.

Examples

\dontrun{
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
}

Method `fit()`

Usage

LMTrainer$fit(X, y)

Arguments

X: data.frame containing train featuers
y: character, name of target variable

Details

Fits the LMTrainer model on given data

Returns

NULL, train the model and saves internally

Examples

\dontrun{
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$fit(X = housing, y = 'MEDV')
}

Method `predict()`

Usage

LMTrainer$predict(df, lambda = NULL)

Arguments

df: data.frame containing test features
lambda: integer, the number of lambda values - default is 100. By default it picks the best value from the model.

Details

Returns predictions for test data

Returns

vector, a vector containing predictions

Examples

\dontrun{
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$fit(X = housing, y = 'MEDV')
predictions <- lf$cv_predict(df = housing)
}

Method `cv_model()`

Usage

LMTrainer$cv_model(X, y, nfolds, parallel, type.measure = "deviance")

Arguments

X: data.frame containing test features
y: character, name of target variable
nfolds: integer, number of folds
parallel: logical, if do parallel computation. Default=FALSE
type.measure: character, evaluation metric type. Default = deviance

Details

Train regression model using cross validation

Returns

NULL, trains the model and saves it in memory

Examples

\dontrun{
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$cv_model(X = housing, y = 'MEDV', nfolds = 5, parallel = FALSE)
}

Method `cv_predict()`

Usage

LMTrainer$cv_predict(df, lambda = NULL)

Arguments

df: data.frame containing test features
lambda: integer, the number of lambda values - default is 100. By default it picks the best value from the model.

Details

Get predictions from the cross validated regression model

Returns

vector a vector containing predicted values

Examples

\dontrun{
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$cv_model(X = housing, y = 'MEDV', nfolds = 5, parallel = FALSE)
predictions <- lf$cv_predict(df = housing)
}

Method `get_importance()`

Usage

LMTrainer$get_importance()

Details

Get feature importance using model coefficients

Returns

a matrix containing feature coefficients

Examples

\dontrun{
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$cv_model(X = housing, y = 'MEDV', nfolds = 5, parallel = FALSE)
predictions <- lf$cv_predict(df = housing)
coefs <- lf$get_importance()
}

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LMTrainer$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `LMTrainer$new`
## ------------------------------------------------

## Not run: 
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)

## End(Not run)

## ------------------------------------------------
## Method `LMTrainer$fit`
## ------------------------------------------------

## Not run: 
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$fit(X = housing, y = 'MEDV')

## End(Not run)

## ------------------------------------------------
## Method `LMTrainer$predict`
## ------------------------------------------------

## Not run: 
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$fit(X = housing, y = 'MEDV')
predictions <- lf$cv_predict(df = housing)

## End(Not run)

## ------------------------------------------------
## Method `LMTrainer$cv_model`
## ------------------------------------------------

## Not run: 
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$cv_model(X = housing, y = 'MEDV', nfolds = 5, parallel = FALSE)

## End(Not run)

## ------------------------------------------------
## Method `LMTrainer$cv_predict`
## ------------------------------------------------

## Not run: 
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$cv_model(X = housing, y = 'MEDV', nfolds = 5, parallel = FALSE)
predictions <- lf$cv_predict(df = housing)

## End(Not run)

## ------------------------------------------------
## Method `LMTrainer$get_importance`
## ------------------------------------------------

## Not run: 
LINK <- "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
housing <- read.table(LINK)
names <- c("CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS",
           "RAD","TAX","PTRATIO","B","LSTAT","MEDV")
names(housing)  <-  names
lf <- LMTrainer$new(family = 'gaussian', alpha=1)
lf$cv_model(X = housing, y = 'MEDV', nfolds = 5, parallel = FALSE)
predictions <- lf$cv_predict(df = housing)
coefs <- lf$get_importance()

## End(Not run)

[Package superml version 0.5.7 Index]

Linear Models Trainer

Description

Details

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Details

Returns

Examples

Method fit()

Usage

Arguments

Details

Returns

Examples

Method predict()

Usage

Arguments

Details

Returns

Examples

Method cv_model()

Usage

Arguments

Details

Returns

Examples

Method cv_predict()

Usage

Arguments

Details

Returns

Examples

Method get_importance()

Usage

Details

Returns

Examples

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `fit()`

Method `predict()`

Method `cv_model()`

Method `cv_predict()`

Method `get_importance()`

Method `clone()`