<jupyter_start><jupyter_text>Unedited 47-dim data   $m_{Z'} = 350$ GeV<jupyter_code>data50_350Gm_train, data50_350Gm_test = DA.gettraintest(0)
randforest_350Gm = sigbg_model(
    make_pipeline(StandardScaler(), RandomForestClassifier(n_jobs=-1)), data50_350Gm_train, data50_350Gm_test);
randforest_350Gm.fit()
results(randforest_350Gm, 350)
indices = np.argsort(-np.abs(randforest_350Gm.model[1].feature_importances_))
coef_sort = [[DA.cols[i], randforest_350Gm.model[1].feature_importances_[i]] for i in indices]
max_col = max([len(name) for name in DA.cols])
for name, val in coef_sort:
    sign = '+' if val >= 0 else '-'
    print('{0:{1}} {2:.4f}'.format(name + ':', max_col + 3, val))<jupyter_output>M b1 b2:       0.1546
M j1 j2:       0.1022
pT b1:         0.0878
M b1 b3:       0.0672
pT b2:         0.0643
M b1 b4:       0.0599
pT b3:         0.0368
M b2 b4:       0.0327
M b2 b3:       0.0316
pT j2:         0.0283
M b3 b4:       0.0276
pT b4:         0.0262
MT b1 l MET:   0.0250
MT l MET:      0.0214
dR j1 j2:      0.0186
dR b1 b2:      0.0166
MT b4 l MET:   0.0135
dR b1 b3:      0.0111
MT b2 l MET:   0.0109
MT b3 l MET:   0.0100
dR b1 b4:      0.0096
sdEta b1 b2:   0.0083
pT j1:         0.0081
dR b2 b3:      0.0079
dR b2 b4:      0.0072
sdEta b1 b3:   0.0072
dR b4 l:       0.0069
dR b1 l:       0.0068
dR b3 b4:      0.0066
sdEta b1 b4:   0.0059
sdEta b2 b3:   0.0055
dR b2 l:       0.0055
MET:           0.0054
pT l:          0.0053
sdEta b2 b4:   0.0053
dR b3 l:       0.0052
sdEta b3 b4:   0.0050
sdPhi b1 b2:   0.0043
sdPhi b1 b4:   0.0043
sdPhi b1 b3:   0.0043
sdPhi b1 l:    0.0042
sdPhi b2 b4:   0.0042
sdPhi b2 b3:   0.0042
sdPhi b3 b4:   0.0042
sdPhi b2 l:    0.0042
sdPhi b3 l[...]<jupyter_text>$m_{Z'} = 500$ GeV<jupyter_code>data50_500Gm_train, data50_500Gm_test = DA.gettraintest(1)
randforest_500Gm = sigbg_model(
    make_pipeline(StandardScaler(), RandomForestClassifier(n_jobs=-1)), data50_500Gm_train, data50_500Gm_test);
randforest_500Gm.fit()
results(randforest_500Gm, 500)
indices = np.argsort(-np.abs(randforest_500Gm.model[1].feature_importances_))
coef_sort = [[DA.cols[i], randforest_500Gm.model[1].feature_importances_[i]] for i in indices]
max_col = max([len(name) for name in DA.cols])
for name, val in coef_sort:
    sign = '+' if val >= 0 else '-'
    print('{0:{1}} {2:.4f}'.format(name + ':', max_col + 3, val))<jupyter_output>M b1 b2:       0.1929
pT b1:         0.1232
pT b2:         0.1111
M b1 b3:       0.0971
M j1 j2:       0.0765
M b1 b4:       0.0470
M b2 b3:       0.0374
pT b3:         0.0318
MT b1 l MET:   0.0269
M b2 b4:       0.0235
pT j2:         0.0205
M b3 b4:       0.0181
pT b4:         0.0159
dR b1 b2:      0.0151
MT l MET:      0.0150
dR j1 j2:      0.0130
MT b2 l MET:   0.0116
dR b1 b3:      0.0085
MT b4 l MET:   0.0085
sdEta b1 b2:   0.0074
MT b3 l MET:   0.0070
dR b1 b4:      0.0059
dR b2 b3:      0.0056
sdEta b1 b3:   0.0053
pT j1:         0.0051
dR b2 b4:      0.0050
dR b1 l:       0.0048
dR b3 b4:      0.0042
dR b4 l:       0.0041
sdEta b1 b4:   0.0039
sdEta b2 b3:   0.0037
dR b2 l:       0.0036
dR b3 l:       0.0033
pT l:          0.0033
MET:           0.0033
sdEta b2 b4:   0.0033
sdEta b3 b4:   0.0030
sdPhi b1 b2:   0.0026
sdPhi b2 b3:   0.0026
sdPhi b1 b4:   0.0025
sdPhi b1 b3:   0.0025
sdPhi b1 l:    0.0025
sdPhi b2 l:    0.0024
sdPhi b2 b4:   0.0024
sdPhi b3 b4:   0.0024
sdPhi b4 l[...]<jupyter_text>$m_{Z'} = 1$ TeV<jupyter_code>data50_1Tm_train, data50_1Tm_test = DA.gettraintest(2)
randforest_1Tm = sigbg_model(
    make_pipeline(StandardScaler(), RandomForestClassifier(n_jobs=-1)), data50_1Tm_train, data50_1Tm_test);
randforest_1Tm.fit()
results(randforest_1Tm, 1000)
indices = np.argsort(-np.abs(randforest_1Tm.model[1].feature_importances_))
coef_sort = [[DA.cols[i], randforest_1Tm.model[1].feature_importances_[i]] for i in indices]
max_col = max([len(name) for name in DA.cols])
for name, val in coef_sort:
    sign = '+' if val >= 0 else '-'
    print('{0:{1}} {2:.4f}'.format(name + ':', max_col + 3, val))<jupyter_output>M b1 b2:       0.2356
pT b1:         0.1890
pT b2:         0.1274
M b1 b3:       0.0781
pT b3:         0.0504
MT b1 l MET:   0.0479
M b1 b4:       0.0441
M b2 b3:       0.0431
M j1 j2:       0.0388
M b2 b4:       0.0279
MT b2 l MET:   0.0173
dR b1 b2:      0.0114
pT b4:         0.0110
M b3 b4:       0.0103
MT l MET:      0.0056
dR b1 b3:      0.0056
pT j2:         0.0056
sdEta b1 b2:   0.0050
MT b4 l MET:   0.0043
dR j1 j2:      0.0037
dR b2 b3:      0.0034
MT b3 l MET:   0.0033
dR b1 b4:      0.0032
sdEta b1 b3:   0.0028
dR b4 l:       0.0020
dR b2 b4:      0.0020
dR b1 l:       0.0019
sdEta b2 b3:   0.0016
sdEta b1 b4:   0.0016
pT j1:         0.0015
dR b3 b4:      0.0013
dR b2 l:       0.0013
sdEta b2 b4:   0.0013
dR b3 l:       0.0011
sdEta b3 b4:   0.0011
sdPhi b1 b2:   0.0010
pT l:          0.0009
MET:           0.0009
sdPhi b2 b3:   0.0007
sdPhi b1 b3:   0.0007
sdPhi b1 l:    0.0007
sdPhi b2 l:    0.0007
sdPhi b1 b4:   0.0007
sdPhi b2 b4:   0.0007
sdPhi b3 b4:   0.0007
sdPhi b3 l[...]<jupyter_text>$m_{Z'} = 2$ TeV<jupyter_code>data50_2Tm_train, data50_2Tm_test = DA.gettraintest(3)
randforest_2Tm = sigbg_model(
    make_pipeline(StandardScaler(), RandomForestClassifier(n_jobs=-1)), data50_2Tm_train, data50_2Tm_test);
randforest_2Tm.fit()
results(randforest_2Tm, 2000)
indices = np.argsort(-np.abs(randforest_2Tm.model[1].feature_importances_))
coef_sort = [[DA.cols[i], randforest_2Tm.model[1].feature_importances_[i]] for i in indices]
max_col = max([len(name) for name in DA.cols])
for name, val in coef_sort:
    sign = '+' if val >= 0 else '-'
    print('{0:{1}} {2:.4f}'.format(name + ':', max_col + 3, val))<jupyter_output>M b1 b2:       0.2874
pT b1:         0.1434
pT b2:         0.1415
M b1 b3:       0.1006
MT b1 l MET:   0.0653
M b2 b4:       0.0480
M b2 b3:       0.0388
MT b2 l MET:   0.0385
M b1 b4:       0.0332
pT b3:         0.0244
dR b1 b2:      0.0192
M j1 j2:       0.0173
M b3 b4:       0.0046
pT b4:         0.0040
dR b1 b3:      0.0032
dR b2 b3:      0.0031
sdEta b1 b2:   0.0023
MT b4 l MET:   0.0022
dR b3 l:       0.0022
dR b2 b4:      0.0021
dR j1 j2:      0.0017
MT l MET:      0.0017
dR b1 b4:      0.0016
sdEta b1 b3:   0.0012
pT j2:         0.0012
MT b3 l MET:   0.0010
dR b4 l:       0.0010
dR b3 b4:      0.0008
sdEta b2 b3:   0.0008
dR b2 l:       0.0008
dR b1 l:       0.0007
sdPhi b1 b2:   0.0007
sdEta b1 b4:   0.0006
pT l:          0.0006
sdEta b2 b4:   0.0005
MET:           0.0005
pT j1:         0.0005
sdEta b3 b4:   0.0004
sdPhi b2 b3:   0.0004
sdPhi b1 b3:   0.0003
sdPhi b1 l:    0.0002
sdPhi b2 l:    0.0002
sdPhi b3 b4:   0.0002
sdPhi b2 b4:   0.0002
sdPhi b1 b4:   0.0002
sdPhi b4 l[...]<jupyter_text>$m_{Z'} = 4$ TeV<jupyter_code>data50_4Tm_train, data50_4Tm_test = DA.gettraintest(4)
randforest_4Tm = sigbg_model(
    make_pipeline(StandardScaler(), RandomForestClassifier(n_jobs=-1)), data50_4Tm_train, data50_4Tm_test);
randforest_4Tm.fit()
results(randforest_4Tm, 4000)
indices = np.argsort(-np.abs(randforest_4Tm.model[1].feature_importances_))
coef_sort = [[DA.cols[i], randforest_4Tm.model[1].feature_importances_[i]] for i in indices]
max_col = max([len(name) for name in DA.cols])
for name, val in coef_sort:
    sign = '+' if val >= 0 else '-'
    print('{0:{1}} {2:.4f}'.format(name + ':', max_col + 3, val))<jupyter_output>M b1 b2:       0.2184
pT b1:         0.1413
pT b2:         0.1352
M b1 b3:       0.1219
M b2 b3:       0.0886
MT b1 l MET:   0.0736
M b2 b4:       0.0532
MT b2 l MET:   0.0370
M b1 b4:       0.0359
pT b3:         0.0329
dR b1 b2:      0.0129
M j1 j2:       0.0105
pT b4:         0.0065
dR b2 b3:      0.0031
M b3 b4:       0.0024
dR b1 b3:      0.0024
sdPhi b1 b2:   0.0022
MT l MET:      0.0014
dR b1 b4:      0.0014
dR j1 j2:      0.0013
dR b3 l:       0.0012
sdEta b1 b2:   0.0012
pT j2:         0.0011
dR b4 l:       0.0011
pT j1:         0.0010
dR b2 b4:      0.0010
sdPhi b1 b3:   0.0010
MT b3 l MET:   0.0009
pT l:          0.0008
dR b1 l:       0.0008
dR b3 b4:      0.0008
MT b4 l MET:   0.0008
dR b2 l:       0.0007
sdEta b1 b3:   0.0006
sdEta b2 b4:   0.0006
sdEta b2 b3:   0.0005
sdEta b1 b4:   0.0004
MET:           0.0004
sdEta b3 b4:   0.0004
sdPhi b1 l:    0.0003
sdPhi b2 l:    0.0003
sdPhi b2 b3:   0.0003
sdPhi b1 b4:   0.0003
sdPhi b3 l:    0.0003
sdPhi b4 l:    0.0003
sdPhi b2 b[...]
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