sederhana

l10n-placeholder1 perlakukan == 1, 2, 1) uji\\(treat <- ifelse(test\\)perlakukan == 1, 2, 1) latih\\(y <- ifelse(train\\)y == 1, 2, 1) uji\\(y <- ifelse(test\\)y == 1, 2, 1) train\\(ts = NULL test\\)ts = NULL ``` Parameter: - `n` = jumlah sampel - `p` = jumlah prediktor - `ro` = kovarian antar prediktor - `sigma` = pengganda kesalahan istilah - `beta.den` = beta dimutilasi oleh 1/beta.den Pencipta: Leo Guelman leo.guelman@gmail.com Untuk menggunakan dataset ini: ```python import tensorflow_datasets as tfds ds = tfds.load('simpte' , split='train') untuk ex di ds.take(4): print(ex) ``` Lihat [panduan](https://www.tensorflow.org/datasets/overview) untuk informasi selengkapnya tentang [tensorflow_datasets ](https://www.tensorflow.org/datasets). " />
  • Deskripsi :

Nama lengkap: Simulasi untuk Efek Perawatan yang Dipersonalisasi

Dihasilkan dengan paket Uplift R: ​​https://rdrr.io/cran/uplift/man/sim_pte.html

Paket dapat diunduh di sini: https://cran.r-project.org/src/contrib/Archive/uplift/

Dataset di-generate di R versi 4.1.2 dengan kode berikut:

  library(uplift)

  set.seed(123)

  train <- sim_pte(n = 1000, p = 20, rho = 0, sigma = sqrt(2), beta.den = 4)
  test <- sim_pte(n = 2000, p = 20, rho = 0, sigma = sqrt(2), beta.den = 4)

  train$treat <- ifelse(train$treat == 1, 2, 1)
  test$treat <- ifelse(test$treat == 1, 2, 1)

  train$y <- ifelse(train$y == 1, 2, 1)
  test$y <- ifelse(test$y == 1, 2, 1)

  train$ts = NULL
  test$ts = NULL

Parameter:

  • n = jumlah sampel
  • p = jumlah prediktor
  • ro = kovarian antara prediktor
  • sigma = pengganda dari istilah kesalahan
  • beta.den = beta dikalikan dengan 1/beta.den

Pencipta: Leo Guelman leo.guelman@gmail.com

  • Beranda : https://rdrr.io/cran/uplift/man/sim_pte.html

  • Kode sumber : tfds.datasets.simpte.Builder

  • Versi :

    • 1.0.0 (default): Rilis awal.

  • Ukuran unduhan : Unknown size

  • Ukuran dataset : 1.04 MiB

  • Instruksi pengunduhan manual : Kumpulan data ini mengharuskan Anda mengunduh data sumber secara manual ke download_config.manual_dir (default ke ~/tensorflow_datasets/downloads/manual/ ):
    Harap unduh data pelatihan: sim_pte_train.csv dan data uji: sim_pte_test.csv ke ~/tensorflow_datasets/downloads/manual/.

  • Di-cache otomatis ( dokumentasi ): Ya

  • Perpecahan :

Membelah Contoh
'test' 2.000
'train' 1.000
  • Struktur fitur :
FeaturesDict({
    'X1': float32,
    'X10': float32,
    'X11': float32,
    'X12': float32,
    'X13': float32,
    'X14': float32,
    'X15': float32,
    'X16': float32,
    'X17': float32,
    'X18': float32,
    'X19': float32,
    'X2': float32,
    'X20': float32,
    'X3': float32,
    'X4': float32,
    'X5': float32,
    'X6': float32,
    'X7': float32,
    'X8': float32,
    'X9': float32,
    'treat': int32,
    'y': int32,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
X1 Tensor float32
X10 Tensor float32
X11 Tensor float32
X12 Tensor float32
X13 Tensor float32
X14 Tensor float32
X15 Tensor float32
X16 Tensor float32
X17 Tensor float32
X18 Tensor float32
X19 Tensor float32
X2 Tensor float32
X20 Tensor float32
X3 Tensor float32
X4 Tensor float32
X5 Tensor float32
X6 Tensor float32
X7 Tensor float32
X8 Tensor float32
X9 Tensor float32
merawat Tensor int32
y Tensor int32

  • Kunci yang diawasi (Lihat as_supervised doc ): ({'X1': 'X1', 'X10': 'X10', 'X11': 'X11', 'X12': 'X12', 'X13': 'X13', 'X14': 'X14', 'X15': 'X15', 'X16': 'X16', 'X17': 'X17', 'X18': 'X18', 'X19': 'X19', 'X2': 'X2', 'X20': 'X20', 'X3': 'X3', 'X4': 'X4', 'X5': 'X5', 'X6': 'X6', 'X7': 'X7', 'X8': 'X8', 'X9': 'X9', 'treat': 'treat'}, 'y')

  • Gambar ( tfds.show_examples ): Tidak didukung.

  • Contoh ( tfds.as_dataframe ):

  • Kutipan :
@misc{https://doi.org/10.48550/arxiv.1212.2995,
  doi = {10.48550/ARXIV.1212.2995},
  url = {https://arxiv.org/abs/1212.2995},
  author = {Tian, Lu and Alizadeh, Ash and Gentles, Andrew and Tibshirani, Robert},
  keywords = {Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates},
  publisher = {arXiv},
  year = {2012},
  copyright = {arXiv.org perpetual, non-exclusive license}
}