Information Theoretic Methods #8
Description
Hi Adam,
I have 500 examples, continues outcome ( above and below 0) and 33000 features. I would like to use feast,... for finding the most informative features. As far as I know, I have to discretise the outcome. Can you please advise how I should that? Also do you aware of any information theoretic packages that can deal with the continues outcome?
Please see some of numbers in the list for the outcome.
0.55228076
-0.3197724
0.58774863
-0.5174945
-0.173138
1.27375815
0.78408593
0.74372201
-1.3886196
0.43545769
-0.0689654
0.18626918
1.8202002
0.10355088
-0.0560193
0.29268956
-0.4401979
1.61399178
-0.704535
0.00430693
1.41157343
-0.0646488
-1.4474665
0.4307273
0.44970806
-0.3885697
1.10758465
-0.1339137
0.12522725
-0.3379575
-1.099666
0.31524279
-0.7666343
0.22144974
-0.8017797
-1.1909404
-0.7958541
-1.5830851
1.03128861
-1.0312886
-1.736507
0.77242535
1.06106625
0.95375794
-0.8626751
-0.4025419
-0.1078819
0.71006597
0.5422753
-2.2562499
-1.3238074
-1.919012
1.24519952
-0.4930275
0.47848871
1.21762188
-0.6239719
1.42333462
-0.0086139
0.89435119
-1.698852
1.77679865
-0.038772
-0.2569066
0.28371043
-0.360848
-0.2658198
0.8440787
-0.9812682
0.67719571
0.31977237
-2.0077154
0.48332361
0.70453501
0.52242483
-0.9537579
-0.8197281
-1.2835121
-0.5076713
0.35625516
0.03015302
0.05601926
-0.1994326
1.13176806
-2.3148972
1.3886196
0.5724601
-1.8925387
-1.1822326
-0.2926896
0.37467286
-0.6610331
-0.2971879
-1.3555018
-1.5536207
0.73805488
-0.3746729
-0.6031758
-0.6935372
1.43529606
1.48532767
0.629213
0.19943255
-0.7211937
-1.068659
-0.4165933
0.01292112
1.26412388
-0.9335899
0.22586574
-2.2044686
-0.6450414
-0.5724601
1.00954199
0.05170609
-1.0840322
-0.5027781
-0.7782424
-0.2258657
-0.5623418
-0.1644006
-0.7156187
-0.8879448
-0.2792295
-0.0776024
-0.4544784
-0.8137162
0.93358993
0.73241136
1.89253869
-1.366375
-1.472472
0.15131773
-0.6135407
-0.1165502
0.80177968
0.92695117
1.71737513
0.77824241
-1.4853277
-0.2347111
-0.5522808
0.85644338
0.09922176
-0.5573042
-1.1566322
0.34252111
0.37005675
1.51180219
-1.5118022
0.02153627
0.37929696
1.28351212
-0.0948945
-0.7551286
1.59834762
0.14696264
-0.4784887
1.56818275
0.60317579
0.82576977
1.05353422
0.33795753
0.83184174
-2.702943
-0.3107197
0.72679107
1.40000431
-0.379297
-0.2702843
-0.4307273
1.18223265
0.3516698
1.13997784
1.73650701
0.60835006
1.47247197
0.03446219
1.55362072
-0.5224248
1.06865897
2.46384107
0.38856968
-0.333401
0.18188866
2.31489724
-0.3654485
-0.5422753
1.36637495
-0.112215
-0.3152428
0.42600652
-0.1426103
0.94027009
-0.7899563
-0.5472712
0.39787592
1.25460554
-1.3135329
0.42129522
0.12088759
0.86267505
0.99530565
-1.4233346
0.97432155
0.19065327
0.26581978
-0.6664013
-2.158
0.9007945
0.0776024
-0.3016924
-1.9763958
0.27475411
1.08403222
0.81972809
0.23028616
2.00771543
1.33422361
0.55730423
-0.9402701
0.76086887
-0.6083501
0.14261033
-0.4354577
1.6634623
-1.009542
-0.473665
-0.6880696
0.34709182
-1.6634623
0.65035348
-0.3562552
0.92035301
0.85024479
-0.6717887
1.04606148
-0.4978969
0.4544784
-1.680894
-2.1157713
-0.1469626
1.94690278
-2.041138
0.49789692
-1.1650809
0.11655021
-1.1317681
1.03864671
-1.6300412
2.15799996
1.16508088
0.30169235
0.23471108
-2.5652793
0.01722854
-2.382787
0.46885224
-0.8318417
0.038772
2.92573583
0.41659325
0.41190048
0.58263735
-0.2747541
-1.6139918
-0.1687677
-0.0430825
0.78995634
0.40721675
-1.0918158
-1.1236339
-1.2176219
0.25690662
-1.5393809
-0.030153
-1.7562965
1.52544651
-0.9882626
-1.3774121
0.83794453
-0.6771957
0.24801382
-1.4115734
0.27028425
-1.7767986
0.56739369
-2.0770094
-1.2546055
-1.7173751
-0.9007945
-0.2126306
-0.1818887
-1.0763138
-0.3470918
-0.2038279
-1.2086323
0.07328321
0.20822723
-0.8564434
1.09966602
0.96742157
1.97639581
-0.2837104
-0.920353
0.71561873
1.91901198
Thanks
Fari