LazyDeepRegressor.RdSee also https://techtonique.github.io/nnetsauce/
LazyDeepRegressor(
verbose = 0,
ignore_warnings = TRUE,
custom_metric = NULL,
predictions = FALSE,
random_state = 42L,
estimators = "all",
preprocess = FALSE,
n_layers = 3L,
...
)monitor progress (0, default, is false and 1 is true)
print trace when model fitting failed
defining a custom metric (default is NULL)
obtain predictions (default is FALSE)
reproducibility seed
specify regressors to be adjusted (default is 'all')
preprocessing input covariates (default is FALSE FALSE)
number of layers for the deep model
additional parameters to be passed to nnetsauce::CustomRegressor
a list that you can $fit
X <- MASS::Boston[,-14] # dataset has an ethical problem
y <- MASS::Boston$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]
obj <- LazyDeepRegressor()
res <- obj$fit(X_train, X_test, y_train, y_test)
print(res[[1]])
#> [1] 0.8526364 0.8514566