How to plot predicted values vs the true value
Question:
I’m new to visualization using matplotlib. I will like to make a plot of my machine learning model’s predicted value vs the actual value.
I made a prediction using random forest algorithm and will like to visualize the plot of true values and predicted values.
I used the below code, but the plot isn’t showing clearly the relationship between the predicted and actual values.
plt.scatter(y_test1, y_pred_test_Forestreg)
plt.xlabel('True Values ')
plt.ylabel('Predictions ')
plt.axis('equal')
plt.axis('square')
plt.xlim([0, plt.xlim()])
plt.ylim([0, plt.ylim()])
_ = plt.plot([-100, 100], [-100, 100])
This is what I get:
This is what I expect the plot to look like:
Below is the table of data i want to plot.(Note: This is just part of the result as it is impossible to include the full data (shape 8221, 1) here. i look forward to your help.
True_value Predicted_value
19.624 15.144
4.685 4.815
2.924 3.038
3.113 3.784
10.512 10.400
9.176 9.066
6.375 5.983
4.412 4.232
8.273 7.917
3.166 3.251
68.971 107.703
181.666 237.296
7.701 8.048
2.447 6.054
131.302 207.189
13.768 13.457
11.623 13.137
8.528 8.807
15.098 17.706
56.473 54.183
59.310 167.495
3.348 3.328
32.844 34.156
578.226 505.921
1.448 1.446
10.062 9.766
7.570 7.265
8.616 8.672
3.674 3.644
3.288 2.931
3.540 3.562
4.560 5.061
5.887 5.541
1.665 1.688
1.871 1.904
1.410 1.439
9.912 403.442
2.935 2.997
12.787 12.957
3.457 3.596
11.299 11.967
8.130 8.460
8.865 11.949
7.540 7.515
60.140 84.870
16.552 17.161
10.865 11.791
6.067 6.578
11.295 16.454
75.891 185.727
10.326 11.284
34.206 107.315
22.264 22.015
3.950 4.260
28.428 27.939
12.290 12.022
5.473 9.635
6.745 7.254
634.100 673.322
15.266 16.482
15.521 43.444
18.474 17.949
3.755 3.572
46.217 69.086
16.910 14.501
2.680 2.753
274.212 316.699
8.235 8.440
6.427 6.307
6.089 5.979
28.649 29.809
4.168 4.382
2.547 2.708
4.315 4.311
7.585 7.409
6.233 6.248
34.533 31.312
10.258 10.079
8.695 9.437
4.033 12.747
4.125 4.098
525.944 219.438
2.579 2.611
523.896 282.774
8.701 8.535
7.240 7.155
176.189 200.219
3.428 3.463
2.585 2.813
27.354 34.487
3.001 3.338
38.852 82.933
12.774 16.158
16.984 17.053
11.137 11.219
4.082 4.084
3.328 3.262
274.311 209.084
6.897 7.223
62.672 114.585
43.145 72.709
2.984 3.033
9.826 10.398
3.516 3.742
7.338 7.184
7.378 7.162
9.957 9.932
6.911 22.346
7.950 10.278
5.116 4.820
7.892 8.124
14.289 18.204
6.993 6.660
19.128 20.634
3.115 3.211
3.542 4.578
21.191 115.314
8.054 8.121
4.050 3.860
9.886 11.048
4.155 6.156
11.709 11.088
8.132 8.471
3.890 3.949
4.378 4.437
6.988 10.504
14.657 25.161
24.183 34.785
10.967 798.643
3.996 4.247
3.198 3.327
156.253 351.941
5.146 5.262
11.318 11.291
16.291 16.949
158.091 203.395
22.975 21.835
5.912 6.467
12.273 12.490
3.539 3.542
16.078 16.097
51.275 80.729
3.488 4.741
2.925 3.088
3.778 3.881
6.571 6.429
2.901 2.811
1.601 2.957
3.696 3.577
31.660 32.617
7.704 8.171
20.296 22.126
4.045 4.334
134.317 190.880
2.555 2.852
5.464 7.617
12.790 12.009
4.284 4.556
6.270 6.779
1.671 1.670
226.813 91.195
72.333 71.087
3.791 3.813
7.525 8.456
2.172 2.399
2.959 2.909
30.524 69.432
2.827 2.830
3.085 3.134
2.872 2.932
3.742 3.929
3.649 3.566
5.980 15.945
2.526 2.584
1.368 1.437
3.601 3.655
10.210 9.142
12.890 13.373
10.297 18.741
4.448 4.461
2.445 2.441
7821.052 209.961
5.288 5.424
6.344 7.370
4.965 4.934
2.613 2.645
3.185 3.252
124.729 167.799
2.405 2.563
30.347 116.190
3.292 3.305
665.125 973.483
4.164 4.251
6.322 6.311
11.213 407.169
95.240 343.970
5.783 5.994
5.373 5.870
19.684 21.314
17.965 25.495
30.212 30.220
61.062 63.275
3.549 3.490
5.964 5.915
21.034 25.812
47.966 47.694
14.440 13.870
5.449 6.194
5.259 5.309
6.598 6.491
104.152 133.026
10.739 10.297
11.346 11.375
17.738 16.433
5.873 5.891
3.844 3.874
3.964 3.939
21.967 22.237
2.246 2.198
17.728 14.837
3.784 3.879
22.299 26.496
644.469 675.529
17.276 18.275
5.231 5.172
18.520 32.575
7.318 7.207
3.755 3.952
283.689 237.367
35.856 38.537
573.022 587.498
28.416 66.907
7.308 7.136
4.968 5.126
10.330 11.180
1.610 1.738
16.856 17.902
42.227 43.223
2.051 2.041
7.593 9.966
7.569 8.319
5.441 5.746
5.972 6.653
3.717 3.599
8.827 9.307
7.706 8.501
3.623 3.295
9.030 13.022
6.355 6.297
7.335 9.017
13.303 12.695
4.113 4.121
3.723 3.737
111.807 898.529
2.851 2.891
11.799 13.946
2394.623 1824.993
4.082 4.092
3.056 3.046
2.624 2.730
3.470 3.984
4.257 5.701
2.896 3.084
2.443 2.478
2.870 2.964
3.321 2.960
2.828 2.932
3.141 3.127
12.469 12.952
16.836 16.809
9.443 9.719
2.554 2.638
5.645 5.492
5.714 5.886
4.343 4.475
14.376 15.382
10.272 27.267
5.985 5.618
4.007 3.866
4.131 4.157
2.406 2.544
6.889 7.021
7.578 7.749
3.672 3.600
906.078 216.823
2.902 3.025
9.181 9.414
8.592 8.066
6.513 18.057
271.303 228.073
5.702 5.848
5.085 5.392
2.616 2.593
3.754 3.874
30.282 35.751
21.143 25.404
14.135 15.484
36.088 40.671
3.123 3.576
4.275 4.755
6.851 6.882
2.818 2.761
2.159 2.164
9.910 9.536
3.049 3.067
4.427 5.804
6.712 6.458
4.494 4.221
3.068 3.197
5.406 5.613
3.227 3.241
24.215 53.796
637.213 286.607
5.956 6.193
1.471 1.628
55.357 40.091
210.939 179.626
10.495 30.618
4.570 4.749
543.716 600.721
149.483 303.777
6.426 8.019
3.584 4.201
5.645 5.716
345.349 248.498
6.279 5.735
3.202 3.244
203.829 195.321
10.781 12.432
4.101 3.965
8.068 8.434
2.857 3.038
3.087 3.080
12.415 12.642
4.565 4.695
549.052 613.451
18.186 23.562
16.835 18.274
4.791 6.422
71.954 70.883
4.768 4.833
3.521 3.604
19.906 17.715
16.679 39.652
130.312 104.834
6.184 6.200
140.157 143.435
3.544 3.559
57.671 98.001
17.373 20.190
7.149 7.182
11.680 11.834
21.702 21.113
22.296 21.578
13.011 13.667
10.163 10.251
4.846 4.961
3.140 3.136
13.378 23.330
2.997 3.053
5.985 5.649
13.253 14.494
11.334 13.650
28.669 28.714
10.286 10.428
9.503 9.448
4.742 4.682
2.221 2.284
3.861 3.902
240.606 291.496
15.891 18.820
8.417 9.890
5.489 5.405
6.948 6.772
5.827 5.797
2.000 2.097
5.365 5.523
21.660 42.945
14.776 14.856
11.559 11.872
113.205 68.657
27.932 58.427
3461.739 1284.346
4.265 4.264
4.679 4.776
4.158 4.167
5.433 5.745
4.630 4.672
3.234 3.273
2.979 3.008
2.973 3.000
65.804 192.535
9.779 9.668
4.859 5.321
25.096 25.863
31.760 32.688
45.694 88.227
9.456 9.014
3.848 3.757
3.219 3.663
3.437 3.555
3.145 3.880
4.071 4.734
9.924 10.470
1.803 2.191
8.169 9.736
2.865 2.903
9.904 56.968
4.630 5.931
9.509 12.341
3.601 3.610
20.892 29.847
12.044 12.784
4.555 4.787
5.870 11.672
6.595 7.227
6.838 6.873
4.685 4.716
5.192 6.754
9.431 16.747
2.668 2.737
13.617 14.081
2.232 2.274
7.903 8.343
2.499 2.615
34.243 48.755
4.698 4.900
3.748 3.432
37.223 66.586
68.727 361.602
25.718 36.754
18.440 18.247
15.377 15.465
3.886 3.931
2.643 2.600
9.831 9.503
39.471 40.691
3.029 3.156
7.123 6.307
9.489 9.209
3.149 3.287
7.776 7.646
3.390 3.544
10.181 14.724
8.250 8.084
193.590 261.347
9.793 12.250
70.579 69.578
7.832 7.399
5.046 5.176
3.968 4.005
9.784 12.865
7.610 7.236
4996.689 2691.915
313.615 422.989
6.895 7.304
3.470 3.484
11.665 18.933
3.292 3.317
1.783 1.947
3.219 3.111
3.985 3.964
3.498 3.610
36.447 36.004
8.682 9.461
5.307 5.283
70.309 68.247
3.070 3.118
24.358 22.845
11.658 16.996
4.120 4.151
4.298 4.632
14.703 27.946
3.584 3.608
821.402 464.270
5.953 6.212
128.394 98.013
19.772 20.482
52.685 56.871
15.331 47.899
3.063 3.138
27.708 29.416
5.710 5.702
5.179 5.176
6.794 7.548
5.535 5.903
7.756 7.542
13.773 15.158
42.209 47.055
9.589 9.636
4.101 4.053
11.070 10.378
9.900 10.381
23.599 27.321
6.342 7.113
237.329 265.999
4.236 4.156
3.725 3.765
3.288 3.761
12.502 13.748
22.315 23.830
460.784 499.877
37.721 59.371
3.329 3.455
2.656 2.734
7.192 13.859
3.141 3.169
16.235 17.393
9.122 11.052
4.592 5.448
4.822 4.917
3.775 3.841
23.833 30.813
3.330 3.408
32.084 43.318
2.922 2.642
9.614 9.788
19.096 19.256
3.442 3.273
4.007 4.938
30.032 30.929
4.988 5.175
3.160 3.197
3.550 3.606
10.242 10.115
3.102 3.137
5.496 5.485
78.592 170.062
20.358 21.758
3.878 4.560
7.540 7.334
3.525 3.586
41.475 42.571
2.526 2.551
284.630 211.248
2.610 2.621
15.534 17.391
20.425 33.944
4.757 4.765
3.913 4.076
3.830 3.574
10.342 9.655
10.169 10.913
30.062 50.935
3.767 3.821
10.695 13.182
3.992 3.987
12.472 12.897
7.534 7.612
5.622 5.747
3.971 3.960
3.435 3.686
1326.840 1219.852
46107.740 316.479
3.811 3.797
2.531 2.616
6.154 5.978
45.078 70.688
36.858 35.887
13.847 14.226
21.346 32.181
16.678 18.144
15.503 15.724
2.691 2.736
27.847 36.464
6.376 6.316
14.914 15.570
9.088 11.115
12.111 13.716
55.573 47.872
16.263 17.161
3.524 3.513
7.709 8.567
5.546 5.526
2.949 2.814
5.711 5.824
1.900 1.992
4.627 4.638
7.726 8.888
1.879 2.139
8.284 8.346
45.501 46.389
9.511 9.486
6.682 7.590
7.960 16.404
2.684 2.647
4.696 4.752
5.750 5.675
15.713 15.559
3.617 3.625
44.469 45.952
20.249 20.487
5.670 6.105
107.327 262.087
8.889 8.471
13.256 13.335
126.793 136.720
137.222 168.966
3.026 3.041
8.653 9.073
3.465 4.198
25.399 44.397
16.268 68.009
7.730 7.676
26.813 63.690
5.427 6.090
3.672 3.716
26.927 32.404
2.879 2.922
488.947 187.509
13.759 17.262
17.620 18.346
3.768 4.381
2.410 2.652
38.413 83.543
3.581 3.688
9.117 8.473
49.507 44.383
12.744 9.823
23.463 15.088
152.177 156.684
35.534 74.871
15.581 12.622
3.262 3.295
3.054 3.089
9.100 11.311
9.668 10.491
2.909 2.924
3.783 3.696
10.671 13.134
5.098 5.271
14.355 131.551
4.601 4.558
73.732 522.207
15.599 16.085
99.343 171.043
9.426 10.030
16.628 18.044
11.698 11.487
3.561 3.583
5.189 5.167
4.687 4.769
12.656 12.308
3.325 3.444
3.948 4.025
60.056 152.943
14.180 16.198
9.861 9.616
63.960 69.110
4.679 4.675
16.040 16.687
7.904 7.643
6.450 6.727
3.803 4.413
2.553 2.739
40.290 97.088
2.708 2.835
425.787 314.400
2.439 2.477
2.785 2.805
3.270 3.284
2.647 2.710
5.165 5.211
48.268 40.837
3.257 3.247
214.791 332.489
5.842 6.338
17.314 17.595
7.217 7.600
11.369 10.983
4.525 12.805
9.691 35.084
7.733 8.054
47.099 44.539
4.428 4.658
3.050 3.160
21.687 21.427
3.499 3.571
4.851 4.774
2.977 3.060
2.545 2.566
3.662 4.037
22.456 22.634
2.181 2.239
326.994 374.272
55.825 55.422
2.393 2.478
4.400 6.259
3.782 3.799
2.809 2.804
9.876 13.799
2.576 2.653
16.874 16.959
21.571 23.953
15.590 17.355
42.106 51.814
10.481 10.497
2.916 2.968
3.334 3.302
2.954 3.059
1.696 1.735
5.395 6.021
5.418 5.255
42.656 49.237
5.596 5.675
3.480 3.554
17.537 21.359
3.228 3.383
58.281 179.127
25.906 63.865
21.146 25.153
4.658 4.720
3.850 3.888
9.028 15.569
4.629 4.711
3.091 3.171
24.311 41.592
2.652 2.698
14.238 14.362
12.500 12.204
3.574 3.627
321.192 6054.332
4.070 4.263
13.435 13.500
2.249 2.341
10.612 10.822
3.224 3.409
27.689 27.566
3.954 4.244
20.670 22.052
6.427 6.765
3.392 3.515
2.920 3.359
14.821 15.202
2.611 2.794
6.555 7.040
9.217 12.450
5.632 5.729
6.226 5.949
4.872 6.035
3.619 4.020
8.413 9.601
1.448 1.504
7.171 7.861
3.952 3.864
3.377 3.390
11.497 12.984
8.768 7.989
11.831 12.099
3.136 3.121
9.831 12.960
9.540 9.640
10.653 11.002
4.646 5.055
18.888 14.569
3.136 3.150
185.894 281.490
30.000 33.611
3.099 3.383
14528.128 194.832
3.533 3.551
60.248 72.399
16.598 15.403
5.506 6.254
2.885 2.785
10.409 10.430
6.957 6.359
10.874 17.594
5.967 6.343
105.277 135.997
173.652 857.814
2.381 3.225
9.035 9.054
2.968 3.385
10.200 10.618
5.132 5.480
462.597 203.613
3.955 4.076
18.293 26.279
3.258 3.353
3.629 3.519
3.624 3.667
4.140 17.326
3.448 3.726
176.988 72.779
21.992 33.420
1.912 1.915
20.365 21.570
2.801 3.024
7.667 9.698
73.205 68.196
11.238 11.440
12.600 12.502
2.826 2.911
13.567 13.484
5.286 5.429
2.749 2.858
7.208 7.190
8.269 8.003
162.883 215.015
4.572 4.541
59.605 95.131
143.216 199.214
11.269 12.128
11.469 14.168
34.084 31.335
6.867 15.177
4.481 4.457
7.499 6.741
4.513 4.767
3.141 3.254
3.221 3.214
2.948 2.875
5.513 5.298
7.164 8.900
13.643 13.920
13.516 15.751
228.455 264.090
18.596 25.985
2.572 2.641
3.588 3.526
184.955 296.952
5.161 5.870
5.834 8.090
3.114 3.125
4.721 4.766
7.596 7.547
17.221 15.741
6.401 6.706
5.301 5.285
5.072 5.416
3.559 7.562
4.951 5.511
13.149 45.857
17.839 20.007
25.825 27.040
2.947 3.143
2.954 2.977
19.163 36.026
6.853 46.787
1234.533 895.424
9.103 9.127
6.063 5.949
4.596 4.656
20.167 36.586
132.208 129.966
64.140 93.127
12.166 11.759
4.699 5.181
4.833 5.464
7.117 36.724
42.634 65.560
4.988 5.685
3.252 3.175
14.238 15.520
5.948 6.027
3.099 3.123
4.190 4.883
40.309 42.843
3.063 3.196
5.789 5.911
2.668 2.714
27.305 24.457
13.130 14.262
5.462 5.335
230.848 297.006
2.131 2.182
2.918 2.999
4.971 5.090
3.121 3.378
2.103 2.115
17.212 16.520
2.063 2.076
17.047 17.497
29.930 48.084
2.474 2.593
19.437 15.786
4.036 4.011
6.311 7.566
32.844 39.152
4.086 4.163
4.841 5.930
216.971 90.661
3.811 4.976
2.958 3.018
10.057 10.921
3.111 3.126
2.402 2.468
103.789 160.448
38.330 41.226
12.148 13.005
3.876 3.643
4.960 4.957
19.842 19.848
16.860 18.693
19.083 25.635
16.207 20.152
10.292 11.449
18.104 19.176
3.244 3.268
6.349 6.967
9.476 9.581
24.041 23.769
3.753 4.275
10.291 13.313
7.082 7.471
9.135 9.262
88.004 113.825
5.438 5.238
427.816 326.175
39.240 72.889
2.434 2.467
2.626 2.742
4.965 5.306
23.282 20.708
2.487 2.595
122.099 118.899
3.201 3.152
8.655 8.895
9.244 9.042
3.264 3.455
21.233 31.791
7.346 9.535
10.145 12.891
3.188 3.207
81.958 75.353
14.312 14.969
111.029 144.639
9.118 10.859
275.693 149.173
4.416 4.747
3.075 3.085
4.944 4.785
3.749 3.844
10.440 15.537
35.442 34.194
1903.978 246.478
7.105 7.157
28.782 42.077
141.881 265.094
4.897 9.252
29.811 39.802
2.399 2.546
15.536 15.934
2.323 2.485
15.379 20.478
8.901 10.844
2.494 2.526
2.943 3.579
3.808 3.828
5.006 5.371
46.338 58.896
6.285 6.131
7.067 7.692
10.146 9.935
18.963 18.006
3.821 3.849
3.374 3.089
4.176 4.267
1.867 1.962
3.029 2.933
10.424 11.745
7.899 14.366
41.736 43.484
203.775 242.494
20.162 38.360
6.337 6.425
4.034 6.067
4.241 4.346
8.871 9.049
2.915 2.928
3.382 3.415
1.808 1.915
2.835 2.913
7.117 7.156
2.290 2.399
8.650 9.025
3.798 3.821
3.474 3.482
2.639 2.792
3.687 3.756
13.404 13.450
6.119 6.688
12.387 16.997
45.936 55.680
11.247 11.161
4.274 4.423
7.325 10.756
29.293 27.371
9.515 19.688
7.857 7.680
5.348 22.322
163.558 178.067
24.362 20.704
20.334 19.389
3.535 3.546
7.405 7.502
30.687 28.936
12.820 13.067
16.036 15.349
4.525 4.644
7.361 7.496
10.054 11.879
7.697 9.671
11.423 11.470
2.973 3.038
1314.315 323.847
112.133 160.072
16.433 23.824
4.906 5.328
7.876 8.760
10.229 9.743
2.814 2.821
257.298 249.414
2.467 2.913
5.176 5.347
5.191 9.566
6.346 6.879
9.219 8.968
8.048 8.219
3.832 3.834
4.459 4.636
25.413 39.491
4.700 4.472
347.022 287.293
1.345 1.381
2.813 2.908
9.625 9.323
3.809 3.995
7.431 22.802
3.661 3.820
5.383 9.702
3.712 3.785
4.763 4.771
8.235 8.958
19.655 23.900
15.520 13.607
7.013 6.968
14.973 15.679
2.384 2.420
4.971 5.077
6.074 6.479
10.907 14.398
10.633 10.592
100.205 272.179
5.507 8.602
3.933 5.477
6.311 6.562
3.729 4.175
19.241 19.845
4.872 4.800
9.470 9.167
13.976 18.381
2.110 2.134
4.407 6.087
12.468 34.135
45.424 50.214
2.512 5.133
22.283 23.099
6.261 6.630
15.590 21.447
23.178 35.645
39.043 36.060
2.670 2.843
19.230 30.284
3.077 3.088
3.273 3.360
3.264 3.304
44.335 210.250
82.392 74.348
3.973 4.747
30.960 70.890
6.265 6.221
7.608 8.167
5.943 797.595
6.186 9.305
10.559 10.650
10.691 11.225
7.879 7.851
21.246 25.182
3.607 3.576
6.703 7.297
106.397 110.987
7.925 15.494
19.990 29.775
7.284 8.833
156.078 174.563
38.052 39.191
5.875 6.148
94.980 570.359
2.569 2.566
2.688 2.770
3.080 3.076
34.402 35.595
3.145 3.269
303.919 241.618
2.988 3.362
2.344 2.479
4.419 4.500
16.500 16.542
3.214 3.219
6.524 6.263
15.548 14.508
49.636 112.217
81.555 95.624
38.713 39.742
35.177 35.511
6.376 6.757
12.303 13.147
15.831 15.487
8.664 8.499
13.038 14.052
76.699 79.075
6.567 6.763
30.068 30.138
4.166 4.190
11.244 11.023
10.033 15.945
8.026 8.410
20.400 24.974
25.895 56.055
5.347 5.551
2.639 2.639
4.799 4.557
10.292 11.111
466.511 201.463
5.570 6.146
3.581 3.887
114.262 240.503
2.394 2.408
14.285 14.559
5.548 6.802
94.413 54.871
5.914 5.657
2.996 2.985
12.743 17.174
64.850 343.782
6.416 6.853
30.839 30.897
6.602 6.345
183.528 206.723
9.141 10.174
3.501 3.512
27.424 87.668
4.738 4.886
2.816 2.760
17.365 30.646
4.007 4.085
7.485 8.774
7.654 7.444
11.835 14.526
294.052 270.140
3.662 3.713
115.129 208.145
4.381 4.253
3.638 4.308
2.752 3.336
3.500 4.949
3.442 3.406
5.175 5.302
5.695 6.043
3.417 3.384
5.643 6.373
7.287 6.973
4.445 5.089
225.768 189.505
3.695 3.759
2.665 2.820
16.550 16.458
17.384 16.734
26.914 31.025
3.397 3.361
3.006 3.054
2.089 2.122
34.676 35.022
10.833 11.133
1049.306 350.535
15.384 28.722
19.489 18.079
775.681 731.995
4.548 5.418
6.270 6.606
68.405 66.981
3.851 4.227
21.010 75.327
26.540 30.676
13.190 13.393
29.683 31.399
86.971 227.074
7.432 7.444
12.055 15.133
99.511 74.751
7.418 8.342
28.807 24.266
52.762 52.212
3.951 4.839
4.244 4.105
3.908 3.852
3.580 3.579
28.467 68.300
11.045 11.432
2.776 2.826
4.181 3.967
7.051 7.164
4.962 4.696
5.242 5.742
2.662 2.931
2.666 2.678
10.889 10.831
2.493 2.534
15.825 18.569
4.334 4.414
16.147 35.420
270.914 298.895
18.300 17.052
5.218 5.480
2.892 2.928
5.884 5.699
4.923 5.001
4.180 4.316
14.932 14.942
41.254 75.577
2.507 2.601
3.261 3.285
3.323 6.875
3.284 3.267
27.438 32.004
19.371 20.212
3.170 3.193
5.018 5.555
42.568 36.890
25.968 30.364
9.335 9.489
272.611 255.764
13.364 13.961
5.729 5.642
12.335 19.017
38.416 207.078
3.702 3.696
48.208 76.352
6.136 7.892
3.452 3.803
3.975 3.951
17.466 19.923
11.703 11.391
82.279 120.894
3.020 3.018
45.694 67.196
3.047 3.248
5.188 5.270
32.589 46.707
3.283 3.296
3.532 3.867
24.104 52.124
11.111 42.011
2.617 2.647
9.136 9.944
3.258 3.267
9.458 24.309
8.300 8.317
16.536 34.283
17.828 18.889
5.224 5.479
20.401 1346.159
18.276 17.085
4.969 5.033
11.977 11.986
10.110 10.653
31.651 31.643
11.656 11.726
Answers:
I would recommend rescaling your values, as the values are too spread out. While majority of your entries are less than 50 but the scales are up to 60000. Once you have rescaled your entries lets say to 0 – 1 scale then it should look better. I would use some kind of scaler for it.
The problem is that the range of your values span from about 0 to 60.000.
I would suggest two options:
Either you convert both axis to a log-scale
g=plt.scatter(y_test1, y_pred_test_Forestreg)
g.axes.set_yscale('log')
g.axes.set_xscale('log')
g.axes.set_xlabel('True Values ')
g.axes.set_ylabel('Predictions ')
g.axes.axis('equal')
g.axes.axis('square')
Or, even better, Plot the difference between the true and predicted values (i.e. the prediction errors).
g=plt.plot(y_test1 - y_pred_test_Forestreg,marker='o',linestyle='')
Actually you can retrieve a first basic information from this scatter plot: the model has real poor prediction on extremely high input values. Rmse here will be quite high and you can already summarize that the model is pretty bad (if these high input values are as important as the low).
If you don’t care about the hi input values, then you could simply give a look to the plot in a smaller domain changing your x and ylim.
Linearly re-scaling both of them by the same factor will just produce the same scatter plot but with different labels. You may normalize each of them to their maximum value, but I don’t know how readable may your scatter plot become.
If your purpose is just better visualize the result, you may apply some non-linear transformation to your actual and predicted values (db? gamma correction?).
This is my try at understanding your problem and to get what you are exactly looking for. So assuming we have the true_value and predicted_value handy. I’d plot them like this:
plt.figure(figsize=(10,10))
plt.scatter(true_value, predicted_value, c='crimson')
plt.yscale('log')
plt.xscale('log')
p1 = max(max(predicted_value), max(true_value))
p2 = min(min(predicted_value), min(true_value))
plt.plot([p1, p2], [p1, p2], 'b-')
plt.xlabel('True Values', fontsize=15)
plt.ylabel('Predictions', fontsize=15)
plt.axis('equal')
plt.show()
Which results in:
Are you looking for something like this by any chance? And yes I am using a logarithmic axis because of the difference in scales of your values.
I hope this is what you wanted.
PS. – I am really not sure what the coloring of the points in the provided chart is or what those bands are, but I can still think
around what those bands mean and create something like that, but not
sure about the marker colorings. So if you can provide the link to
where you got that chart from, I think I might be able to understand
what exactly is being done in that chart.
To plot the predicted label vs. the actual label I would do the following:
- Assume these are the names of my parameters
X_features_main #The X Features
y_label_main #The Y Label
y_predicted_from_X_features_main #The predicted Y-label from X-features I used
plt.scatter(x=X_features_main, y=y_label_main,color='black') #The X-Features vs. The Real Label
plt.plot(X_features_main, y_predicted_from_X_features_main,color='blue') #The X- Features vs. The predicted label
plt.show()#To show your figures code here
I’m new to visualization using matplotlib. I will like to make a plot of my machine learning model’s predicted value vs the actual value.
I made a prediction using random forest algorithm and will like to visualize the plot of true values and predicted values.
I used the below code, but the plot isn’t showing clearly the relationship between the predicted and actual values.
plt.scatter(y_test1, y_pred_test_Forestreg)
plt.xlabel('True Values ')
plt.ylabel('Predictions ')
plt.axis('equal')
plt.axis('square')
plt.xlim([0, plt.xlim()])
plt.ylim([0, plt.ylim()])
_ = plt.plot([-100, 100], [-100, 100])
This is what I get:
This is what I expect the plot to look like:
Below is the table of data i want to plot.(Note: This is just part of the result as it is impossible to include the full data (shape 8221, 1) here. i look forward to your help.
True_value Predicted_value
19.624 15.144
4.685 4.815
2.924 3.038
3.113 3.784
10.512 10.400
9.176 9.066
6.375 5.983
4.412 4.232
8.273 7.917
3.166 3.251
68.971 107.703
181.666 237.296
7.701 8.048
2.447 6.054
131.302 207.189
13.768 13.457
11.623 13.137
8.528 8.807
15.098 17.706
56.473 54.183
59.310 167.495
3.348 3.328
32.844 34.156
578.226 505.921
1.448 1.446
10.062 9.766
7.570 7.265
8.616 8.672
3.674 3.644
3.288 2.931
3.540 3.562
4.560 5.061
5.887 5.541
1.665 1.688
1.871 1.904
1.410 1.439
9.912 403.442
2.935 2.997
12.787 12.957
3.457 3.596
11.299 11.967
8.130 8.460
8.865 11.949
7.540 7.515
60.140 84.870
16.552 17.161
10.865 11.791
6.067 6.578
11.295 16.454
75.891 185.727
10.326 11.284
34.206 107.315
22.264 22.015
3.950 4.260
28.428 27.939
12.290 12.022
5.473 9.635
6.745 7.254
634.100 673.322
15.266 16.482
15.521 43.444
18.474 17.949
3.755 3.572
46.217 69.086
16.910 14.501
2.680 2.753
274.212 316.699
8.235 8.440
6.427 6.307
6.089 5.979
28.649 29.809
4.168 4.382
2.547 2.708
4.315 4.311
7.585 7.409
6.233 6.248
34.533 31.312
10.258 10.079
8.695 9.437
4.033 12.747
4.125 4.098
525.944 219.438
2.579 2.611
523.896 282.774
8.701 8.535
7.240 7.155
176.189 200.219
3.428 3.463
2.585 2.813
27.354 34.487
3.001 3.338
38.852 82.933
12.774 16.158
16.984 17.053
11.137 11.219
4.082 4.084
3.328 3.262
274.311 209.084
6.897 7.223
62.672 114.585
43.145 72.709
2.984 3.033
9.826 10.398
3.516 3.742
7.338 7.184
7.378 7.162
9.957 9.932
6.911 22.346
7.950 10.278
5.116 4.820
7.892 8.124
14.289 18.204
6.993 6.660
19.128 20.634
3.115 3.211
3.542 4.578
21.191 115.314
8.054 8.121
4.050 3.860
9.886 11.048
4.155 6.156
11.709 11.088
8.132 8.471
3.890 3.949
4.378 4.437
6.988 10.504
14.657 25.161
24.183 34.785
10.967 798.643
3.996 4.247
3.198 3.327
156.253 351.941
5.146 5.262
11.318 11.291
16.291 16.949
158.091 203.395
22.975 21.835
5.912 6.467
12.273 12.490
3.539 3.542
16.078 16.097
51.275 80.729
3.488 4.741
2.925 3.088
3.778 3.881
6.571 6.429
2.901 2.811
1.601 2.957
3.696 3.577
31.660 32.617
7.704 8.171
20.296 22.126
4.045 4.334
134.317 190.880
2.555 2.852
5.464 7.617
12.790 12.009
4.284 4.556
6.270 6.779
1.671 1.670
226.813 91.195
72.333 71.087
3.791 3.813
7.525 8.456
2.172 2.399
2.959 2.909
30.524 69.432
2.827 2.830
3.085 3.134
2.872 2.932
3.742 3.929
3.649 3.566
5.980 15.945
2.526 2.584
1.368 1.437
3.601 3.655
10.210 9.142
12.890 13.373
10.297 18.741
4.448 4.461
2.445 2.441
7821.052 209.961
5.288 5.424
6.344 7.370
4.965 4.934
2.613 2.645
3.185 3.252
124.729 167.799
2.405 2.563
30.347 116.190
3.292 3.305
665.125 973.483
4.164 4.251
6.322 6.311
11.213 407.169
95.240 343.970
5.783 5.994
5.373 5.870
19.684 21.314
17.965 25.495
30.212 30.220
61.062 63.275
3.549 3.490
5.964 5.915
21.034 25.812
47.966 47.694
14.440 13.870
5.449 6.194
5.259 5.309
6.598 6.491
104.152 133.026
10.739 10.297
11.346 11.375
17.738 16.433
5.873 5.891
3.844 3.874
3.964 3.939
21.967 22.237
2.246 2.198
17.728 14.837
3.784 3.879
22.299 26.496
644.469 675.529
17.276 18.275
5.231 5.172
18.520 32.575
7.318 7.207
3.755 3.952
283.689 237.367
35.856 38.537
573.022 587.498
28.416 66.907
7.308 7.136
4.968 5.126
10.330 11.180
1.610 1.738
16.856 17.902
42.227 43.223
2.051 2.041
7.593 9.966
7.569 8.319
5.441 5.746
5.972 6.653
3.717 3.599
8.827 9.307
7.706 8.501
3.623 3.295
9.030 13.022
6.355 6.297
7.335 9.017
13.303 12.695
4.113 4.121
3.723 3.737
111.807 898.529
2.851 2.891
11.799 13.946
2394.623 1824.993
4.082 4.092
3.056 3.046
2.624 2.730
3.470 3.984
4.257 5.701
2.896 3.084
2.443 2.478
2.870 2.964
3.321 2.960
2.828 2.932
3.141 3.127
12.469 12.952
16.836 16.809
9.443 9.719
2.554 2.638
5.645 5.492
5.714 5.886
4.343 4.475
14.376 15.382
10.272 27.267
5.985 5.618
4.007 3.866
4.131 4.157
2.406 2.544
6.889 7.021
7.578 7.749
3.672 3.600
906.078 216.823
2.902 3.025
9.181 9.414
8.592 8.066
6.513 18.057
271.303 228.073
5.702 5.848
5.085 5.392
2.616 2.593
3.754 3.874
30.282 35.751
21.143 25.404
14.135 15.484
36.088 40.671
3.123 3.576
4.275 4.755
6.851 6.882
2.818 2.761
2.159 2.164
9.910 9.536
3.049 3.067
4.427 5.804
6.712 6.458
4.494 4.221
3.068 3.197
5.406 5.613
3.227 3.241
24.215 53.796
637.213 286.607
5.956 6.193
1.471 1.628
55.357 40.091
210.939 179.626
10.495 30.618
4.570 4.749
543.716 600.721
149.483 303.777
6.426 8.019
3.584 4.201
5.645 5.716
345.349 248.498
6.279 5.735
3.202 3.244
203.829 195.321
10.781 12.432
4.101 3.965
8.068 8.434
2.857 3.038
3.087 3.080
12.415 12.642
4.565 4.695
549.052 613.451
18.186 23.562
16.835 18.274
4.791 6.422
71.954 70.883
4.768 4.833
3.521 3.604
19.906 17.715
16.679 39.652
130.312 104.834
6.184 6.200
140.157 143.435
3.544 3.559
57.671 98.001
17.373 20.190
7.149 7.182
11.680 11.834
21.702 21.113
22.296 21.578
13.011 13.667
10.163 10.251
4.846 4.961
3.140 3.136
13.378 23.330
2.997 3.053
5.985 5.649
13.253 14.494
11.334 13.650
28.669 28.714
10.286 10.428
9.503 9.448
4.742 4.682
2.221 2.284
3.861 3.902
240.606 291.496
15.891 18.820
8.417 9.890
5.489 5.405
6.948 6.772
5.827 5.797
2.000 2.097
5.365 5.523
21.660 42.945
14.776 14.856
11.559 11.872
113.205 68.657
27.932 58.427
3461.739 1284.346
4.265 4.264
4.679 4.776
4.158 4.167
5.433 5.745
4.630 4.672
3.234 3.273
2.979 3.008
2.973 3.000
65.804 192.535
9.779 9.668
4.859 5.321
25.096 25.863
31.760 32.688
45.694 88.227
9.456 9.014
3.848 3.757
3.219 3.663
3.437 3.555
3.145 3.880
4.071 4.734
9.924 10.470
1.803 2.191
8.169 9.736
2.865 2.903
9.904 56.968
4.630 5.931
9.509 12.341
3.601 3.610
20.892 29.847
12.044 12.784
4.555 4.787
5.870 11.672
6.595 7.227
6.838 6.873
4.685 4.716
5.192 6.754
9.431 16.747
2.668 2.737
13.617 14.081
2.232 2.274
7.903 8.343
2.499 2.615
34.243 48.755
4.698 4.900
3.748 3.432
37.223 66.586
68.727 361.602
25.718 36.754
18.440 18.247
15.377 15.465
3.886 3.931
2.643 2.600
9.831 9.503
39.471 40.691
3.029 3.156
7.123 6.307
9.489 9.209
3.149 3.287
7.776 7.646
3.390 3.544
10.181 14.724
8.250 8.084
193.590 261.347
9.793 12.250
70.579 69.578
7.832 7.399
5.046 5.176
3.968 4.005
9.784 12.865
7.610 7.236
4996.689 2691.915
313.615 422.989
6.895 7.304
3.470 3.484
11.665 18.933
3.292 3.317
1.783 1.947
3.219 3.111
3.985 3.964
3.498 3.610
36.447 36.004
8.682 9.461
5.307 5.283
70.309 68.247
3.070 3.118
24.358 22.845
11.658 16.996
4.120 4.151
4.298 4.632
14.703 27.946
3.584 3.608
821.402 464.270
5.953 6.212
128.394 98.013
19.772 20.482
52.685 56.871
15.331 47.899
3.063 3.138
27.708 29.416
5.710 5.702
5.179 5.176
6.794 7.548
5.535 5.903
7.756 7.542
13.773 15.158
42.209 47.055
9.589 9.636
4.101 4.053
11.070 10.378
9.900 10.381
23.599 27.321
6.342 7.113
237.329 265.999
4.236 4.156
3.725 3.765
3.288 3.761
12.502 13.748
22.315 23.830
460.784 499.877
37.721 59.371
3.329 3.455
2.656 2.734
7.192 13.859
3.141 3.169
16.235 17.393
9.122 11.052
4.592 5.448
4.822 4.917
3.775 3.841
23.833 30.813
3.330 3.408
32.084 43.318
2.922 2.642
9.614 9.788
19.096 19.256
3.442 3.273
4.007 4.938
30.032 30.929
4.988 5.175
3.160 3.197
3.550 3.606
10.242 10.115
3.102 3.137
5.496 5.485
78.592 170.062
20.358 21.758
3.878 4.560
7.540 7.334
3.525 3.586
41.475 42.571
2.526 2.551
284.630 211.248
2.610 2.621
15.534 17.391
20.425 33.944
4.757 4.765
3.913 4.076
3.830 3.574
10.342 9.655
10.169 10.913
30.062 50.935
3.767 3.821
10.695 13.182
3.992 3.987
12.472 12.897
7.534 7.612
5.622 5.747
3.971 3.960
3.435 3.686
1326.840 1219.852
46107.740 316.479
3.811 3.797
2.531 2.616
6.154 5.978
45.078 70.688
36.858 35.887
13.847 14.226
21.346 32.181
16.678 18.144
15.503 15.724
2.691 2.736
27.847 36.464
6.376 6.316
14.914 15.570
9.088 11.115
12.111 13.716
55.573 47.872
16.263 17.161
3.524 3.513
7.709 8.567
5.546 5.526
2.949 2.814
5.711 5.824
1.900 1.992
4.627 4.638
7.726 8.888
1.879 2.139
8.284 8.346
45.501 46.389
9.511 9.486
6.682 7.590
7.960 16.404
2.684 2.647
4.696 4.752
5.750 5.675
15.713 15.559
3.617 3.625
44.469 45.952
20.249 20.487
5.670 6.105
107.327 262.087
8.889 8.471
13.256 13.335
126.793 136.720
137.222 168.966
3.026 3.041
8.653 9.073
3.465 4.198
25.399 44.397
16.268 68.009
7.730 7.676
26.813 63.690
5.427 6.090
3.672 3.716
26.927 32.404
2.879 2.922
488.947 187.509
13.759 17.262
17.620 18.346
3.768 4.381
2.410 2.652
38.413 83.543
3.581 3.688
9.117 8.473
49.507 44.383
12.744 9.823
23.463 15.088
152.177 156.684
35.534 74.871
15.581 12.622
3.262 3.295
3.054 3.089
9.100 11.311
9.668 10.491
2.909 2.924
3.783 3.696
10.671 13.134
5.098 5.271
14.355 131.551
4.601 4.558
73.732 522.207
15.599 16.085
99.343 171.043
9.426 10.030
16.628 18.044
11.698 11.487
3.561 3.583
5.189 5.167
4.687 4.769
12.656 12.308
3.325 3.444
3.948 4.025
60.056 152.943
14.180 16.198
9.861 9.616
63.960 69.110
4.679 4.675
16.040 16.687
7.904 7.643
6.450 6.727
3.803 4.413
2.553 2.739
40.290 97.088
2.708 2.835
425.787 314.400
2.439 2.477
2.785 2.805
3.270 3.284
2.647 2.710
5.165 5.211
48.268 40.837
3.257 3.247
214.791 332.489
5.842 6.338
17.314 17.595
7.217 7.600
11.369 10.983
4.525 12.805
9.691 35.084
7.733 8.054
47.099 44.539
4.428 4.658
3.050 3.160
21.687 21.427
3.499 3.571
4.851 4.774
2.977 3.060
2.545 2.566
3.662 4.037
22.456 22.634
2.181 2.239
326.994 374.272
55.825 55.422
2.393 2.478
4.400 6.259
3.782 3.799
2.809 2.804
9.876 13.799
2.576 2.653
16.874 16.959
21.571 23.953
15.590 17.355
42.106 51.814
10.481 10.497
2.916 2.968
3.334 3.302
2.954 3.059
1.696 1.735
5.395 6.021
5.418 5.255
42.656 49.237
5.596 5.675
3.480 3.554
17.537 21.359
3.228 3.383
58.281 179.127
25.906 63.865
21.146 25.153
4.658 4.720
3.850 3.888
9.028 15.569
4.629 4.711
3.091 3.171
24.311 41.592
2.652 2.698
14.238 14.362
12.500 12.204
3.574 3.627
321.192 6054.332
4.070 4.263
13.435 13.500
2.249 2.341
10.612 10.822
3.224 3.409
27.689 27.566
3.954 4.244
20.670 22.052
6.427 6.765
3.392 3.515
2.920 3.359
14.821 15.202
2.611 2.794
6.555 7.040
9.217 12.450
5.632 5.729
6.226 5.949
4.872 6.035
3.619 4.020
8.413 9.601
1.448 1.504
7.171 7.861
3.952 3.864
3.377 3.390
11.497 12.984
8.768 7.989
11.831 12.099
3.136 3.121
9.831 12.960
9.540 9.640
10.653 11.002
4.646 5.055
18.888 14.569
3.136 3.150
185.894 281.490
30.000 33.611
3.099 3.383
14528.128 194.832
3.533 3.551
60.248 72.399
16.598 15.403
5.506 6.254
2.885 2.785
10.409 10.430
6.957 6.359
10.874 17.594
5.967 6.343
105.277 135.997
173.652 857.814
2.381 3.225
9.035 9.054
2.968 3.385
10.200 10.618
5.132 5.480
462.597 203.613
3.955 4.076
18.293 26.279
3.258 3.353
3.629 3.519
3.624 3.667
4.140 17.326
3.448 3.726
176.988 72.779
21.992 33.420
1.912 1.915
20.365 21.570
2.801 3.024
7.667 9.698
73.205 68.196
11.238 11.440
12.600 12.502
2.826 2.911
13.567 13.484
5.286 5.429
2.749 2.858
7.208 7.190
8.269 8.003
162.883 215.015
4.572 4.541
59.605 95.131
143.216 199.214
11.269 12.128
11.469 14.168
34.084 31.335
6.867 15.177
4.481 4.457
7.499 6.741
4.513 4.767
3.141 3.254
3.221 3.214
2.948 2.875
5.513 5.298
7.164 8.900
13.643 13.920
13.516 15.751
228.455 264.090
18.596 25.985
2.572 2.641
3.588 3.526
184.955 296.952
5.161 5.870
5.834 8.090
3.114 3.125
4.721 4.766
7.596 7.547
17.221 15.741
6.401 6.706
5.301 5.285
5.072 5.416
3.559 7.562
4.951 5.511
13.149 45.857
17.839 20.007
25.825 27.040
2.947 3.143
2.954 2.977
19.163 36.026
6.853 46.787
1234.533 895.424
9.103 9.127
6.063 5.949
4.596 4.656
20.167 36.586
132.208 129.966
64.140 93.127
12.166 11.759
4.699 5.181
4.833 5.464
7.117 36.724
42.634 65.560
4.988 5.685
3.252 3.175
14.238 15.520
5.948 6.027
3.099 3.123
4.190 4.883
40.309 42.843
3.063 3.196
5.789 5.911
2.668 2.714
27.305 24.457
13.130 14.262
5.462 5.335
230.848 297.006
2.131 2.182
2.918 2.999
4.971 5.090
3.121 3.378
2.103 2.115
17.212 16.520
2.063 2.076
17.047 17.497
29.930 48.084
2.474 2.593
19.437 15.786
4.036 4.011
6.311 7.566
32.844 39.152
4.086 4.163
4.841 5.930
216.971 90.661
3.811 4.976
2.958 3.018
10.057 10.921
3.111 3.126
2.402 2.468
103.789 160.448
38.330 41.226
12.148 13.005
3.876 3.643
4.960 4.957
19.842 19.848
16.860 18.693
19.083 25.635
16.207 20.152
10.292 11.449
18.104 19.176
3.244 3.268
6.349 6.967
9.476 9.581
24.041 23.769
3.753 4.275
10.291 13.313
7.082 7.471
9.135 9.262
88.004 113.825
5.438 5.238
427.816 326.175
39.240 72.889
2.434 2.467
2.626 2.742
4.965 5.306
23.282 20.708
2.487 2.595
122.099 118.899
3.201 3.152
8.655 8.895
9.244 9.042
3.264 3.455
21.233 31.791
7.346 9.535
10.145 12.891
3.188 3.207
81.958 75.353
14.312 14.969
111.029 144.639
9.118 10.859
275.693 149.173
4.416 4.747
3.075 3.085
4.944 4.785
3.749 3.844
10.440 15.537
35.442 34.194
1903.978 246.478
7.105 7.157
28.782 42.077
141.881 265.094
4.897 9.252
29.811 39.802
2.399 2.546
15.536 15.934
2.323 2.485
15.379 20.478
8.901 10.844
2.494 2.526
2.943 3.579
3.808 3.828
5.006 5.371
46.338 58.896
6.285 6.131
7.067 7.692
10.146 9.935
18.963 18.006
3.821 3.849
3.374 3.089
4.176 4.267
1.867 1.962
3.029 2.933
10.424 11.745
7.899 14.366
41.736 43.484
203.775 242.494
20.162 38.360
6.337 6.425
4.034 6.067
4.241 4.346
8.871 9.049
2.915 2.928
3.382 3.415
1.808 1.915
2.835 2.913
7.117 7.156
2.290 2.399
8.650 9.025
3.798 3.821
3.474 3.482
2.639 2.792
3.687 3.756
13.404 13.450
6.119 6.688
12.387 16.997
45.936 55.680
11.247 11.161
4.274 4.423
7.325 10.756
29.293 27.371
9.515 19.688
7.857 7.680
5.348 22.322
163.558 178.067
24.362 20.704
20.334 19.389
3.535 3.546
7.405 7.502
30.687 28.936
12.820 13.067
16.036 15.349
4.525 4.644
7.361 7.496
10.054 11.879
7.697 9.671
11.423 11.470
2.973 3.038
1314.315 323.847
112.133 160.072
16.433 23.824
4.906 5.328
7.876 8.760
10.229 9.743
2.814 2.821
257.298 249.414
2.467 2.913
5.176 5.347
5.191 9.566
6.346 6.879
9.219 8.968
8.048 8.219
3.832 3.834
4.459 4.636
25.413 39.491
4.700 4.472
347.022 287.293
1.345 1.381
2.813 2.908
9.625 9.323
3.809 3.995
7.431 22.802
3.661 3.820
5.383 9.702
3.712 3.785
4.763 4.771
8.235 8.958
19.655 23.900
15.520 13.607
7.013 6.968
14.973 15.679
2.384 2.420
4.971 5.077
6.074 6.479
10.907 14.398
10.633 10.592
100.205 272.179
5.507 8.602
3.933 5.477
6.311 6.562
3.729 4.175
19.241 19.845
4.872 4.800
9.470 9.167
13.976 18.381
2.110 2.134
4.407 6.087
12.468 34.135
45.424 50.214
2.512 5.133
22.283 23.099
6.261 6.630
15.590 21.447
23.178 35.645
39.043 36.060
2.670 2.843
19.230 30.284
3.077 3.088
3.273 3.360
3.264 3.304
44.335 210.250
82.392 74.348
3.973 4.747
30.960 70.890
6.265 6.221
7.608 8.167
5.943 797.595
6.186 9.305
10.559 10.650
10.691 11.225
7.879 7.851
21.246 25.182
3.607 3.576
6.703 7.297
106.397 110.987
7.925 15.494
19.990 29.775
7.284 8.833
156.078 174.563
38.052 39.191
5.875 6.148
94.980 570.359
2.569 2.566
2.688 2.770
3.080 3.076
34.402 35.595
3.145 3.269
303.919 241.618
2.988 3.362
2.344 2.479
4.419 4.500
16.500 16.542
3.214 3.219
6.524 6.263
15.548 14.508
49.636 112.217
81.555 95.624
38.713 39.742
35.177 35.511
6.376 6.757
12.303 13.147
15.831 15.487
8.664 8.499
13.038 14.052
76.699 79.075
6.567 6.763
30.068 30.138
4.166 4.190
11.244 11.023
10.033 15.945
8.026 8.410
20.400 24.974
25.895 56.055
5.347 5.551
2.639 2.639
4.799 4.557
10.292 11.111
466.511 201.463
5.570 6.146
3.581 3.887
114.262 240.503
2.394 2.408
14.285 14.559
5.548 6.802
94.413 54.871
5.914 5.657
2.996 2.985
12.743 17.174
64.850 343.782
6.416 6.853
30.839 30.897
6.602 6.345
183.528 206.723
9.141 10.174
3.501 3.512
27.424 87.668
4.738 4.886
2.816 2.760
17.365 30.646
4.007 4.085
7.485 8.774
7.654 7.444
11.835 14.526
294.052 270.140
3.662 3.713
115.129 208.145
4.381 4.253
3.638 4.308
2.752 3.336
3.500 4.949
3.442 3.406
5.175 5.302
5.695 6.043
3.417 3.384
5.643 6.373
7.287 6.973
4.445 5.089
225.768 189.505
3.695 3.759
2.665 2.820
16.550 16.458
17.384 16.734
26.914 31.025
3.397 3.361
3.006 3.054
2.089 2.122
34.676 35.022
10.833 11.133
1049.306 350.535
15.384 28.722
19.489 18.079
775.681 731.995
4.548 5.418
6.270 6.606
68.405 66.981
3.851 4.227
21.010 75.327
26.540 30.676
13.190 13.393
29.683 31.399
86.971 227.074
7.432 7.444
12.055 15.133
99.511 74.751
7.418 8.342
28.807 24.266
52.762 52.212
3.951 4.839
4.244 4.105
3.908 3.852
3.580 3.579
28.467 68.300
11.045 11.432
2.776 2.826
4.181 3.967
7.051 7.164
4.962 4.696
5.242 5.742
2.662 2.931
2.666 2.678
10.889 10.831
2.493 2.534
15.825 18.569
4.334 4.414
16.147 35.420
270.914 298.895
18.300 17.052
5.218 5.480
2.892 2.928
5.884 5.699
4.923 5.001
4.180 4.316
14.932 14.942
41.254 75.577
2.507 2.601
3.261 3.285
3.323 6.875
3.284 3.267
27.438 32.004
19.371 20.212
3.170 3.193
5.018 5.555
42.568 36.890
25.968 30.364
9.335 9.489
272.611 255.764
13.364 13.961
5.729 5.642
12.335 19.017
38.416 207.078
3.702 3.696
48.208 76.352
6.136 7.892
3.452 3.803
3.975 3.951
17.466 19.923
11.703 11.391
82.279 120.894
3.020 3.018
45.694 67.196
3.047 3.248
5.188 5.270
32.589 46.707
3.283 3.296
3.532 3.867
24.104 52.124
11.111 42.011
2.617 2.647
9.136 9.944
3.258 3.267
9.458 24.309
8.300 8.317
16.536 34.283
17.828 18.889
5.224 5.479
20.401 1346.159
18.276 17.085
4.969 5.033
11.977 11.986
10.110 10.653
31.651 31.643
11.656 11.726
I would recommend rescaling your values, as the values are too spread out. While majority of your entries are less than 50 but the scales are up to 60000. Once you have rescaled your entries lets say to 0 – 1 scale then it should look better. I would use some kind of scaler for it.
The problem is that the range of your values span from about 0 to 60.000.
I would suggest two options:
Either you convert both axis to a log-scale
g=plt.scatter(y_test1, y_pred_test_Forestreg)
g.axes.set_yscale('log')
g.axes.set_xscale('log')
g.axes.set_xlabel('True Values ')
g.axes.set_ylabel('Predictions ')
g.axes.axis('equal')
g.axes.axis('square')
Or, even better, Plot the difference between the true and predicted values (i.e. the prediction errors).
g=plt.plot(y_test1 - y_pred_test_Forestreg,marker='o',linestyle='')
Actually you can retrieve a first basic information from this scatter plot: the model has real poor prediction on extremely high input values. Rmse here will be quite high and you can already summarize that the model is pretty bad (if these high input values are as important as the low).
If you don’t care about the hi input values, then you could simply give a look to the plot in a smaller domain changing your x and ylim.
Linearly re-scaling both of them by the same factor will just produce the same scatter plot but with different labels. You may normalize each of them to their maximum value, but I don’t know how readable may your scatter plot become.
If your purpose is just better visualize the result, you may apply some non-linear transformation to your actual and predicted values (db? gamma correction?).
This is my try at understanding your problem and to get what you are exactly looking for. So assuming we have the true_value and predicted_value handy. I’d plot them like this:
plt.figure(figsize=(10,10))
plt.scatter(true_value, predicted_value, c='crimson')
plt.yscale('log')
plt.xscale('log')
p1 = max(max(predicted_value), max(true_value))
p2 = min(min(predicted_value), min(true_value))
plt.plot([p1, p2], [p1, p2], 'b-')
plt.xlabel('True Values', fontsize=15)
plt.ylabel('Predictions', fontsize=15)
plt.axis('equal')
plt.show()
Which results in:
Are you looking for something like this by any chance? And yes I am using a logarithmic axis because of the difference in scales of your values.
I hope this is what you wanted.
PS. – I am really not sure what the coloring of the points in the provided chart is or what those bands are, but I can still think
around what those bands mean and create something like that, but not
sure about the marker colorings. So if you can provide the link to
where you got that chart from, I think I might be able to understand
what exactly is being done in that chart.
To plot the predicted label vs. the actual label I would do the following:
- Assume these are the names of my parameters
X_features_main #The X Features
y_label_main #The Y Label
y_predicted_from_X_features_main #The predicted Y-label from X-features I used
plt.scatter(x=X_features_main, y=y_label_main,color='black') #The X-Features vs. The Real Label
plt.plot(X_features_main, y_predicted_from_X_features_main,color='blue') #The X- Features vs. The predicted label
plt.show()#To show your figures code here