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 [e-mail] 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). " />

Peringatan: Diperlukan unduhan manual. Lihat petunjuk di bawah ini.

• 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 [e-mail]

• 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}
}