我正在使用在这里找到的这段代码对具有2个类的二进制分类进行逻辑回归。
https://github.com/datafleets/horizontal-federated-learning-blog/blob/master/horizontal-fl.ipynb
我正在测试的数据要求进行多类分类（6个类）。
如果我有6个类，是否可以使用上面找到的相同代码？这样，预测就属于以下类别或活动之一：步行，上楼，楼下，坐着，站立，躺下。
https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
任何建议都将受到高度赞赏。
更新： 我试图更改set_weights和train_model中的模型，
model=linear_model.SGDClassifier()
到
model = LogisticRegression(solver='lbfgs', multi_class='ovr', max_iter=1000, random_state=20)
但是它不起作用!!!!!!
TypeError: '(array([402, 286, 419, 194, 644, 151, 127, 567, 359, 7, 616, 209, 465,
546, 144, 182, 582, 528, 263, 192, 185, 690, 241, 211, 36, 296,
143, 637, 401, 264, 74, 365, 385, 700, 28, 514, 406, 606, 328,
410, 673, 726, 260, 276, 572, 103, 398, 267, 256, 550, 563, 321,
46, 203, 463, 195, 702, 405, 343, 392, 476, 670, 60, 427, 474,
197, 645, 55, 610, 175, 647, 367, 641, 364, 573, 593, 213, 251,
366, 489, 262, 706, 268, 140, 730, 345, 425, 512, 487, 608, 61,
319, 115, 284, 99, 409, 456, 464, 310, 727, 313, 447, 125, 400,
481, 181, 722, 322, 352, 325, 375, 171, 347, 714, 318, 294, 655,
383, 164, 141, 89, 253, 513, 293, 633, 530, 568, 350, 157, 218,
5, 468, 155, 547, 1, 496, 76, 624, 395, 643, 397, 139, 266,
721, 255, 196, 170, 583, 622, 349, 459, 49, 515, 179, 215, 27,
698, 704, 581, 71, 537, 661, 107, 129, 500, 399, 38, 339, 600,
564, 679, 326, 615, 231, 305, 176, 617, 225, 467, 353, 35, 723,
82, 92, 334, 16, 230, 686, 440, 455, 101, 435, 521, 189, 295,
220, 205, 362, 363, 242, 493, 154, 495, 420, 597, 710, 660, 79,
344, 628, 707, 720, 450, 333, 243, 239, 236, 559, 689, 50, 120,
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540, 370, 542, 109, 407, 41, 130, 575, 638, 177, 183, 40, 691,
439, 158, 671, 53, 556, 548, 279, 228, 506, 156, 659, 636, 95,
371, 19, 412, 39, 348, 377, 566, 14, 443, 240, 390, 428, 100,
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309, 437, 75, 393, 574, 162, 557, 713, 529, 2, 32, 672, 486,
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355, 346, 45, 701, 519, 135, 525, 303, 152, 577, 684, 651, 91,
6, 591, 460, 161, 222, 254, 387, 160, 283, 683, 488, 607, 479,
78, 538, 394, 199, 269, 31, 457, 630, 68, 535, 623, 658, 180,
611, 0, 63, 511, 482, 212, 13, 9, 248, 214, 668, 190, 579,
517, 149, 484, 552, 444, 682, 124, 386, 697, 118, 543, 297, 112,
424, 308, 21, 138, 411, 648, 639, 470, 210, 56, 280, 18, 33,
667, 324, 445, 508, 272, 249, 545, 287, 202, 43, 632, 709, 678,
235, 587, 430, 654, 656, 223, 687, 491, 25, 187, 85, 677, 458,
408, 3, 134, 30, 23, 15, 614, 657, 292, 131, 233, 12, 724,
198, 145, 59, 178, 734, 304, 403, 712, 396, 596, 332, 142, 281,
301, 216, 116, 376, 106, 88, 646, 174, 503, 603, 34, 389, 358,
110, 341, 167, 416, 302, 146, 681, 485, 317, 570, 361, 438, 219,
388, 422, 70, 415, 73, 132, 618, 733, 191, 728, 426, 380, 505,
688, 261, 111, 588, 699, 77, 122, 137, 166, 612, 227, 94, 695,
592, 717, 330, 716, 446, 311, 93, 494, 173, 452, 674, 282, 24,
431, 391, 57, 539, 207, 69, 413, 555, 441, 90, 676, 153, 421,
201, 4, 629, 432, 472, 594, 554, 451, 731, 478, 119, 483, 634,
289, 666, 273, 703, 640, 22, 578, 705, 580, 265, 449, 369, 354,
384, 662, 105, 584, 234, 586, 277, 680, 585, 469, 298, 480, 121,
536, 417, 527, 8, 315, 306, 605, 275, 11, 454, 516, 340, 54,
549, 693, 113, 58, 589, 184, 665, 320, 47, 102, 448, 337, 52,
274, 627, 418, 259, 87, 569, 685, 471, 379, 381, 37, 526, 631,
590, 669, 725, 378, 128, 533, 65, 72, 342, 335, 504, 221, 48,
675, 252, 307, 692, 245, 708, 188, 200, 561, 150, 715, 83, 599,
732, 531, 114, 285, 625, 20, 126, 382, 635, 104, 123, 291, 598,
541, 649, 51, 534, 414, 331, 694, 163, 206, 258, 652, 224, 576,
204, 80, 64, 518, 133, 602, 650, 492, 558, 250, 290, 312, 327,
208, 338, 246, 271, 696, 98, 44, 117, 609, 323, 466, 81, 462,
247, 621, 544, 613, 604, 523, 147, 429, 193, 595, 244, 619, 520,
300, 565, 653, 172, 473, 226, 168, 620, 368, 67, 169, 490, 510,
507, 232, 560, 663, 357, 97, 136]),)' is an invalid key