DeepRegressor.RdSee also https://techtonique.github.io/nnetsauce/
DeepRegressor(obj, n_layers = 3L, ...)a model object
number of hidden layers
additional parameters to be passed to nnetsauce::CustomRegressor
X <- MASS::Boston[,-14] # dataset has an ethical problem
y <- MASS::Boston$medv
set.seed(13)
(index_train <- base::sample.int(n = nrow(X),
size = floor(0.8*nrow(X)),
replace = FALSE))
#> [1] 472 259 448 357 74 205 262 432 406 132 320 221 248 337 384 362 399 55
#> [19] 279 5 396 305 54 184 32 482 125 47 81 444 485 267 68 376 87 334
#> [37] 61 373 131 223 387 197 446 324 66 385 62 307 127 371 374 60 476 332
#> [55] 440 222 298 182 318 119 15 157 113 14 441 377 160 287 402 381 435 465
#> [73] 210 78 455 333 129 232 361 97 415 304 490 190 124 303 136 398 139 105
#> [91] 340 273 153 224 147 309 7 329 91 90 46 9 164 468 101 487 313 246
#> [109] 67 326 49 264 423 116 170 1 302 442 414 103 437 57 314 355 237 451
#> [127] 154 86 53 123 31 460 452 311 383 226 322 277 130 405 109 295 212 421
#> [145] 317 200 20 99 16 386 498 165 489 84 495 478 291 134 436 348 316 58
#> [163] 126 328 12 217 420 203 339 64 471 73 494 458 502 434 135 100 177 204
#> [181] 285 209 82 143 215 159 183 196 216 315 346 22 25 172 233 352 412 231
#> [199] 359 416 380 354 236 500 319 240 251 13 2 464 35 417 456 121 390 201
#> [217] 418 401 77 18 475 486 146 241 299 244 114 447 503 149 424 228 275 477
#> [235] 33 330 111 207 301 92 335 50 454 388 137 505 150 404 351 438 202 430
#> [253] 96 484 327 162 431 155 282 499 496 270 235 479 158 419 56 457 189 397
#> [271] 239 108 79 166 363 40 245 409 283 450 341 462 156 44 280 422 501 342
#> [289] 191 370 144 269 194 185 429 392 306 358 199 85 94 181 186 349 80 145
#> [307] 43 161 375 331 229 175 426 408 238 344 379 110 72 343 93 413 174 308
#> [325] 480 59 234 128 151 169 104 167 242 470 473 297 179 278 107 83 102 51
#> [343] 133 247 19 353 118 289 98 4 284 443 214 288 336 117 300 17 30 378
#> [361] 463 293 365 491 497 400 323 26 266 428 29 28 389 198 372 70 286 411
#> [379] 321 410 350 265 173 176 459 488 369 220 192 211 115 37 338 481 493 445
#> [397] 206 48 347 27 258 6 274 142
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]
obj2 <- sklearn$linear_model$ElasticNet()
obj <- DeepRegressor(obj2, n_layers = 3L, n_clusters=2L)
res <- obj$fit(X_train, y_train)
print(obj$predict(X_test))
#> [1] 29.315669 22.350606 22.330615 22.563430 16.585819 19.344594 17.857167
#> [8] 17.761656 22.518857 22.486509 22.466146 29.616464 27.565161 23.745390
#> [15] 24.653210 25.119870 27.640567 20.647673 25.075405 24.755254 24.069539
#> [22] 24.051667 28.306331 23.939802 19.175335 25.479655 21.061032 20.854455
#> [29] 20.773257 18.542058 16.694723 13.716292 20.244535 34.319716 22.120471
#> [36] 21.886033 26.184724 28.427947 31.544154 28.731103 31.169815 28.673535
#> [43] 19.685673 22.230741 26.149512 22.935773 33.109097 32.649279 27.210842
#> [50] 24.969324 24.303149 26.319012 26.159760 27.971901 32.346760 25.998563
#> [57] 24.409340 32.087756 29.474692 29.947781 35.144454 34.523309 21.693235
#> [64] 24.992741 28.851445 33.313550 26.346295 29.168505 25.147861 27.749879
#> [71] 22.787596 24.498395 25.007895 28.277187 22.605238 19.322557 18.843306
#> [78] 13.022979 15.203887 10.780382 17.754336 16.736964 11.517881 18.895927
#> [85] 17.292050 17.680457 9.125354 14.910919 16.300847 19.925434 9.862155
#> [92] 17.676539 18.492482 19.470292 18.048893 16.096250 16.990529 22.987573
#> [99] 25.023488 17.874354 26.186417 22.207180