https://github.com/smaakage85/modelgrid

A Minimalistic Framework for Creating, Managing and Training Multiple Caret Models
https://github.com/smaakage85/modelgrid

caret machine-learning predictive-analytics predictive-modeling

Last synced: 3 months ago
JSON representation

A Minimalistic Framework for Creating, Managing and Training Multiple Caret Models

• Host: GitHub
• URL: https://github.com/smaakage85/modelgrid
• Owner: smaakage85
• License: other
• Created: 2018-06-10T09:12:55.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-05-06T21:42:23.000Z (6 months ago)
• Last Synced: 2024-07-10T21:03:26.589Z (4 months ago)
• Topics: caret, machine-learning, predictive-analytics, predictive-modeling
• Language: R
• Homepage:
• Size: 138 KB
• Stars: 23
• Watchers: 3
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-"

)

```

# modelgrid 

[![Travis-CI Build Status](https://travis-ci.org/smaakage85/modelgrid.svg?branch=master)](https://travis-ci.org/smaakage85/modelgrid)

[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/smaakage85/modelgrid?branch=master&svg=true)](https://ci.appveyor.com/project/smaakage85/modelgrid)

This is a small package offering a minimalistic but flexible framework for 

creating, managing and training multiple caret models with a bare minimum of code.

## Installation

`modelgrid` can be installed from CRAN with `install.packages('modelgrid')`. 

If you want the development version then install directly from GitHub:

```{r, eval = FALSE}

# install.packages("devtools")

devtools::install_github("smaakage85/modelgrid")

```

## Building your first model grid

First, pre-allocate an empty model grid with the constructor function `model_grid`.

```{r, message = FALSE}

library(modelgrid)

mg <- model_grid()

mg

```

As you see, a `model_grid` has three components:

* `shared_settings`: settings to be shared across models. Obvious choices include

the target variable, features and resampling scheme.

* `models`: settings that uniquely identify the indvidual models.

* `model_fits`: contains the fitted models, once the `model_grid` has been trained.

Next, decide what settings you want to be shared by the models constituting the

`model_grid`.

```{r, message = FALSE}

library(dplyr)

library(lattice)

library(caret)

data(GermanCredit)

mg <-

  mg %>%

  share_settings(

    y = GermanCredit[["Class"]],

    x = GermanCredit %>% select(-Class),

    metric = "ROC",

    trControl = trainControl(

      method = "cv",

      number = 5,

      summaryFunction = twoClassSummary,

      classProbs = TRUE

      )

  )

```

Our first model candidate will be a simple Random Forest configuration.

```{r}

mg <-

  mg %>%

  add_model(

    model_name = "Funky Forest",

    method = "rf",

    tuneLength = 5

  )

```

Let us also give an eXtreme Gradient Boosting model a shot.

```{r}

mg <-

  mg %>%

  add_model(

    model_name = "Big Boost",

    method = "xgbTree",

    nthread = 8

  )

```

That's it. We are all set to train our first very own (extremely simple)

model grid.

```{r, message = FALSE}

mg <- train(mg)

```

Visualize performance statistics of final models.

```{r performance_bwplot}

mg$model_fits %>%

  resamples(.) %>%

  bwplot(.)

```

You want to know more about all of the exciting features of the `model_grid`? Take a look at the vignette (: