Image classification with GEE and scikit-learn#

So far, we have come a long way. We have learned to work with both, vector and raster data. We have used this knowledge to e.g., extract for specific geo-locations raster-values, to use these data for parameterizing a machine-learning classifier, and to do a subsequent prediction. Now, what this small tutorial does is show you how to do these steps using a combination of functions from the packages scikit-learn and geemap. We will exemplify this on a - admittedly not great - example. Specifically, we will collect some training data from an existing land cover dataset, and predict the same land cover categories on spectra-temporal metrics (STM). Obviously, one would expect the results to be similar, but for the sake of this tutorial, we keep it this way.

We now go step by step over the process with some example code. First, we load some packages and initialize GEE

from osgeo import ogr
from tqdm.notebook import tqdm
import json
import pandas as pd
import ee
import geemap
try:
    ee.Initialize()
except Exception as e:
    ee.Authenticate()
    ee.Initialize()

Load vector data#

Open them using the ogr library#

First, we will load two vector files, both are located on my personal drive:

  1. One file does only contain one feature, with a squared geometry. This will be our region of interest (ROI), i.e., the boundaries of our classification

  2. A set of points that will serve as our training locations. This is not an uncommon scenario: often you have/receive some shapefiles in the form of points, which you use for your work.

roiSHP = ogr.Open("D:/OneDrive - Conservation Biogeography Lab/_TEACHING/__Classes-Modules_HUB/M8_Geoprocessing-with-python/10_Classification-and-LargeArea/01_ROI_shape.shp")
roiLYR = roiSHP.GetLayer()
roiFEAT = roiLYR[0]
roiGEOM = roiFEAT.geometry()
print(roiGEOM)
POLYGON ((13.2134494446947 52.4151979479578,13.2134494446947 52.4873242002239,13.3018503384978 52.4873242002239,13.3018503384978 52.4151979479578,13.2134494446947 52.4151979479578))
ptsSHP = ogr.Open("D:/OneDrive - Conservation Biogeography Lab/_TEACHING/__Classes-Modules_HUB/M8_Geoprocessing-with-python/10_Classification-and-LargeArea/02_RandomPoints_1000.shp")
ptsLYR = ptsSHP.GetLayer()
ptsLYR.GetFeatureCount()
1000

Convert items into ee.Geometry() and ee.FeatureCollection()#

Next, we will convert these items into GEE-Objects. The ROI we convert it into an ee.Geometry(), the points into an ee.FeatureCollection(). Both items we have already learned how to do (check the intro chapter on GEE of this course

geomJSON = json.loads(roiGEOM.ExportToJson())
geomCOORD = geomJSON['coordinates']
geomEE = ee.Geometry.Polygon(coords=geomCOORD)
geomEE
    • type:Polygon
          • 0:13.213449444694733
          • 1:52.41519794795775
          • 0:13.301850338497838
          • 1:52.41519794795775
          • 0:13.301850338497838
          • 1:52.48732420022388
          • 0:13.213449444694733
          • 1:52.48732420022388
          • 0:13.213449444694733
          • 1:52.41519794795775
# Create an empty list that we populate
featList = []
# Iterate over all features in `ptsLYR`
for feat in tqdm(ptsLYR):
    pid = feat.GetField('Id') # Get the ID value
    # Get the geometry
    g = feat.geometry()
    gjson = json.loads(g.ExportToJson())
    gcoord = gjson['coordinates']
    gEE = ee.Geometry.Point(coords=gcoord)
    # Create ee.Feature(), append to list
    eeFeat = ee.Feature(gEE, {"UniqueID": pid})
    featList.append(eeFeat)
# Convert the list into ee.FeatureCollection()
fc = ee.FeatureCollection(ee.List(featList))
fc
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      • UniqueID:Integer
      • system:index:String
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        • type:Feature
        • id:972
          • type:Point
            • 0:13.213999650693252
            • 1:52.48608136297972
          • UniqueID:972
        • type:Feature
        • id:973
          • type:Point
            • 0:13.264985315062408
            • 1:52.45441198550656
          • UniqueID:973
        • type:Feature
        • id:974
          • type:Point
            • 0:13.27564993050549
            • 1:52.43915205443586
          • UniqueID:974
        • type:Feature
        • id:975
          • type:Point
            • 0:13.254387102056459
            • 1:52.428385697624805
          • UniqueID:975
        • type:Feature
        • id:976
          • type:Point
            • 0:13.25122335484987
            • 1:52.422948674994615
          • UniqueID:976
        • type:Feature
        • id:977
          • type:Point
            • 0:13.26502458750207
            • 1:52.481708117583146
          • UniqueID:977
        • type:Feature
        • id:978
          • type:Point
            • 0:13.253350366252164
            • 1:52.416369785231446
          • UniqueID:978
        • type:Feature
        • id:979
          • type:Point
            • 0:13.287901460857352
            • 1:52.44114919960838
          • UniqueID:979
        • type:Feature
        • id:980
          • type:Point
            • 0:13.292237067081317
            • 1:52.467240549835886
          • UniqueID:980
        • type:Feature
        • id:981
          • type:Point
            • 0:13.244010795587668
            • 1:52.41853760298383
          • UniqueID:981
        • type:Feature
        • id:982
          • type:Point
            • 0:13.29293751108423
            • 1:52.477491173606175
          • UniqueID:982
        • type:Feature
        • id:983
          • type:Point
            • 0:13.292515100285321
            • 1:52.485693015965055
          • UniqueID:983
        • type:Feature
        • id:984
          • type:Point
            • 0:13.27550659877582
            • 1:52.45105983996468
          • UniqueID:984
        • type:Feature
        • id:985
          • type:Point
            • 0:13.29120905305513
            • 1:52.474746144622294
          • UniqueID:985
        • type:Feature
        • id:986
          • type:Point
            • 0:13.252598834198372
            • 1:52.464906489187015
          • UniqueID:986
        • type:Feature
        • id:987
          • type:Point
            • 0:13.292938860085643
            • 1:52.46997304432208
          • UniqueID:987
        • type:Feature
        • id:988
          • type:Point
            • 0:13.222246576083842
            • 1:52.47850260354722
          • UniqueID:988
        • type:Feature
        • id:989
          • type:Point
            • 0:13.264377113148946
            • 1:52.4387744828512
          • UniqueID:989
        • type:Feature
        • id:990
          • type:Point
            • 0:13.271976170808243
            • 1:52.459065714600584
          • UniqueID:990
        • type:Feature
        • id:991
          • type:Point
            • 0:13.248963522012994
            • 1:52.42961321629085
          • UniqueID:991
        • type:Feature
        • id:992
          • type:Point
            • 0:13.230256207332912
            • 1:52.47642332687743
          • UniqueID:992
        • type:Feature
        • id:993
          • type:Point
            • 0:13.28786215664712
            • 1:52.47851342832895
          • UniqueID:993
        • type:Feature
        • id:994
          • type:Point
            • 0:13.227653163516132
            • 1:52.465869481513614
          • UniqueID:994
        • type:Feature
        • id:995
          • type:Point
            • 0:13.273377286495446
            • 1:52.427858563869975
          • UniqueID:995
        • type:Feature
        • id:996
          • type:Point
            • 0:13.230064760371574
            • 1:52.48235822403012
          • UniqueID:996
        • type:Feature
        • id:997
          • type:Point
            • 0:13.219688114864947
            • 1:52.481601225033494
          • UniqueID:997
        • type:Feature
        • id:998
          • type:Point
            • 0:13.275080230877274
            • 1:52.44235414762006
          • UniqueID:998
        • type:Feature
        • id:999
          • type:Point
            • 0:13.27987008187485
            • 1:52.451333411363926
          • UniqueID:999

Prepare STM#

From our chapters on image classifications and image workflows in GEE we learned what STMs are and how we can use them. Here, we repeat many of these steps, while at the same time providing some smaller functions that you can incorporate in your library.

Specifically, we take all Landsat 8 and Landsat 9 imagery of the year 2023 with less than 70% cloud cover. We merge the images of these two satellites into one collection, mask and scale the data, and calculate basic STMs.

def ScaleMask_l89(img):
    ## Scale the bands
    refl   = img.select(['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7']).multiply(0.0000275).add(-0.2)
    img_SR = ee.Image(refl).addBands(img.select(['QA_PIXEL']))
    ## mask cloud
    # Get the QA band and apply the bits
    qa = ee.Image(img_SR).select(['QA_PIXEL'])
    dilatedCloud = qa.bitwiseAnd(1 << 1)
    cirrusMask   = qa.bitwiseAnd(1 << 2)
    cloud        = qa.bitwiseAnd(1 << 3)
    cloudShadow  = qa.bitwiseAnd(1 << 4)
    snow         = qa.bitwiseAnd(1 << 5)
    water        = qa.bitwiseAnd(1 << 7)
    # Apply the bits
    clear = dilatedCloud.Or(cloud).Or(cloudShadow).eq(0)
    cfmask = (clear.eq(0).where(water.gt(0), 1)
        .where(snow.gt(0), 3)
        .where(dilatedCloud.Or(cloud).gt(0), 4)
        .where(cloudShadow.gt(0), 2)
        .rename('cfmask'))
    img_SR_cl = ee.Image(img_SR).select(['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7']).mask(cfmask.lte(2))
    return img_SR_cl
# Get the collections, merge them
l8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")\
    .filterDate('2023-03-01', '2023-11-30').filter(ee.Filter.lt('CLOUD_COVER', 70)).filterBounds(geomEE)
l9 = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")\
    .filterDate('2022-03-01', '2023-11-30').filter(ee.Filter.lt('CLOUD_COVER', 70)).filterBounds(geomEE)
lAll = l8.merge(l9)
# Apply masking and filtering
lALL_masked = lAll.map(ScaleMask_l89)
# Define reducers
mean = ee.Reducer.mean().unweighted()
sd = ee.Reducer.stdDev().unweighted()
percentiles = ee.Reducer.percentile([10, 25, 50, 75, 90]).unweighted()
allMetrics = mean.combine(sd, sharedInputs=True).combine(percentiles, sharedInputs=True)
# Apply reducers
stm = lALL_masked.reduce(allMetrics)

We can check the outcome by having a quick look at the band names. I do that here by getting the band names for all metrics calculated for the blue band:

bn = stm.bandNames().getInfo()
bn[0:7]
['SR_B2_mean',
 'SR_B2_stdDev',
 'SR_B2_p10',
 'SR_B2_p25',
 'SR_B2_p50',
 'SR_B2_p75',
 'SR_B2_p90']

Prepare Land Cover variables#

That seemed to have worked well. In essence, we have now our independent variables prepared. As dependent variable - again: thematically not the most suitable case - we extract the label of the ESA World Cover, and add it as an additional band to the SMT image:

# Is an image collection from which we take the first image
esaWC21 = ee.ImageCollection("ESA/WorldCover/v200").select(['Map']).toBands()
# Add to the median composite
allRas = esaWC21.addBands(stm)

Extract values at the point locations#

Now that we have everything ready, we can extract the data for our point locations and store them on our computers. We use the function.sampleRegions() to do that. This function works well over smaller areas; for larger areas we need some workaround (see next chapter).

vals = allRas.sampleRegions(collection=fc, properties=['UniqueID'], scale=30, tileScale=4, geometries=False).getInfo()
#vals

the code box below then allows you to convert the json dictionary into a pandas data frame. It is probably not the smartest option/solution, but it does the job. Feel free to write us with a better option:

flag = 0
featureValues = vals['features']
for f in featureValues:         
 # Chekc here always the right order of the indices
    prop = f['properties']
    if flag == 0:
        out_pd = pd.DataFrame(prop, index=[0])
        flag = 1
    else:
        out_pd = pd.concat([out_pd, pd.DataFrame(prop, index=[0])])
out_pd.head(10)
2021_Map SR_B2_mean SR_B2_p10 SR_B2_p25 SR_B2_p50 SR_B2_p75 SR_B2_p90 SR_B2_stdDev SR_B3_mean SR_B3_p10 ... SR_B6_p90 SR_B6_stdDev SR_B7_mean SR_B7_p10 SR_B7_p25 SR_B7_p50 SR_B7_p75 SR_B7_p90 SR_B7_stdDev UniqueID
0 10 0.028306 0.018597 0.024840 0.028635 0.036197 0.040763 0.025688 0.047771 0.038425 ... 0.167455 0.036227 0.083280 0.053963 0.068867 0.091376 0.100589 0.105140 0.023167 0
0 50 0.036657 0.005947 0.029350 0.040515 0.049012 0.053825 0.016171 0.057013 0.016535 ... 0.196385 0.055205 0.091604 0.015600 0.071095 0.112372 0.122410 0.128213 0.038036 1
0 10 0.034006 0.021100 0.032485 0.038672 0.043595 0.047665 0.026872 0.057391 0.038892 ... 0.196990 0.038173 0.107042 0.072690 0.095515 0.115480 0.127085 0.131815 0.025410 2
0 10 0.026150 0.015408 0.019532 0.026119 0.031921 0.037751 0.008153 0.043462 0.030203 ... 0.156001 0.025951 0.076330 0.053041 0.065664 0.078011 0.089314 0.094718 0.015321 3
0 10 0.029545 0.004077 0.024070 0.031963 0.039470 0.043760 0.015874 0.048170 0.017030 ... 0.177163 0.044934 0.091166 0.064248 0.083085 0.099062 0.110447 0.119660 0.030530 4
0 10 0.033462 0.023300 0.035098 0.041560 0.048710 0.052835 0.033882 0.056933 0.042550 ... 0.199053 0.040876 0.114159 0.083112 0.102665 0.123455 0.134097 0.140835 0.029419 5
0 10 0.038473 0.026408 0.029955 0.041546 0.045932 0.051708 0.010142 0.055444 0.038123 ... 0.169820 0.030425 0.087320 0.062130 0.070242 0.093879 0.102555 0.107532 0.020293 6
0 10 0.039117 0.030752 0.036720 0.040818 0.046042 0.049122 0.011361 0.059865 0.048380 ... 0.161185 0.033179 0.095838 0.074918 0.085175 0.101730 0.110007 0.115783 0.023472 7
0 80 0.011843 -0.002275 0.010925 0.014651 0.021595 0.025775 0.017440 0.027680 0.012822 ... 0.037875 0.012331 0.016576 0.006662 0.012190 0.016480 0.018350 0.027177 0.009429 8
0 10 0.013181 0.001465 0.011200 0.016453 0.018542 0.027535 0.013549 0.034636 0.019065 ... 0.133217 0.033203 0.053111 0.032320 0.040378 0.052175 0.059435 0.065622 0.021263 9

10 rows × 44 columns

Parameterization in scikit-learn and conversion into GEE-classifier#

The data are ready. We can now move on with the parameterization and the prediction. We will use some pretty cool tools from the geemap, including the upload of trained trees beack into GEE. We use a random forest classifier to parameterize a model, similar to what we did in the machine learning session session.

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
# Create the two arrays
y = out_pd['2021_Map']
X = out_pd.drop(['2021_Map', 'UniqueID'], axis=1)
# Create a classifier instance
RF = RandomForestClassifier(random_state=1)
param_grid = {'n_estimators': [10, 25, 50, 75, 100],# 'numberOfTrees
              'max_features': [5, 10, 'sqrt'],# 'variablesPerSplit'
              'min_samples_split': [5, 10, 15], # minLeafPopulation
              'max_depth': [5, 10, 20]} # maxNodes
RF_cv = GridSearchCV(RF, param_grid=param_grid, cv=3, refit=True, n_jobs=4).fit(X, y)
best_mod = RF_cv.best_estimator_

Now, we apply a pretty cool part, implemented in the geemap package. What the two functions below are doing is that they (1) convert the random forest model into strings (i.e., the trees), (2) convert it into a feature collection, before (3) converting it into a ee.Classifer. For very large models with very long variable names the maximum size of 10MB per model might not be sufficient, but for the problem here it is fine.

Note

We have found a way to circumvent this problem, and some of it is related to the points we cover in the large-area-section!

from geemap import ml
feature_names = X.columns.tolist() # get the names of the variables
best_mod_str = ml.rf_to_strings(best_mod, feature_names)
ee_classifier = ml.strings_to_classifier(best_mod_str)
ee_classifier
    • type:Classifier.decisionTreeEnsemble
      • 0:1) root 641 9999 9999 (22.833329736236294) 2) SR_B7_p90 <= 0.138016 641 0.3244 10 4) SR_B5_p90 <= 0.262055 501 0.1855 10 8) SR_B4_p25 <= 0.014431 102 0.5430 10 16) SR_B4_stdDev <= 0.024481 10 0.0000 80 * 17) SR_B4_stdDev > 0.024481 1 0.0000 10 * 9) SR_B4_p25 > 0.014431 102 0.5430 10 18) SR_B4_mean <= 0.050185 91 0.4583 10 36) SR_B5_p25 <= 0.121186 49 0.2392 10 72) SR_B7_p50 <= 0.069218 5 0.0000 10 * 73) SR_B7_p50 > 0.069218 16 0.4764 10 146) SR_B2_p90 <= 0.040680 5 0.0000 50 * 147) SR_B2_p90 > 0.040680 11 0.4800 50 294) SR_B3_p50 <= 0.051749 4 0.0000 10 * 295) SR_B3_p50 > 0.051749 2 0.0000 50 * 37) SR_B5_p25 > 0.121186 49 0.2392 10 74) SR_B6_p90 <= 0.138216 33 0.0400 10 148) SR_B3_p25 <= 0.039993 6 0.0000 10 * 149) SR_B3_p25 > 0.039993 1 0.0000 50 * 75) SR_B6_p90 > 0.138216 26 0.0000 10 * 19) SR_B4_mean > 0.050185 91 0.4583 10 38) SR_B5_p10 <= 0.123881 42 0.4787 50 76) SR_B5_mean <= 0.180165 27 0.3141 50 152) SR_B2_p90 <= 0.042137 1 0.0000 10 * 153) SR_B2_p90 > 0.042137 14 0.0000 50 * 77) SR_B5_mean > 0.180165 27 0.3141 50 154) SR_B4_mean <= 0.062183 12 0.4753 50 308) SR_B4_p50 <= 0.057826 9 0.4861 10 616) SR_B2_p50 <= 0.038618 2 0.0000 10 * 617) SR_B2_p50 > 0.038618 3 0.0000 50 * 309) SR_B4_p50 > 0.057826 4 0.0000 10 * 155) SR_B4_mean > 0.062183 3 0.0000 50 * 39) SR_B5_p10 > 0.123881 42 0.4787 50 78) SR_B7_stdDev <= 0.022885 2 0.0000 50 * 79) SR_B7_stdDev > 0.022885 15 0.3512 10 158) SR_B5_p90 <= 0.221176 2 0.0000 50 * 159) SR_B5_p90 > 0.221176 13 0.2550 10 318) SR_B7_mean <= 0.090081 2 0.4444 10 * 319) SR_B7_mean > 0.090081 9 0.0000 10 * 5) SR_B5_p90 > 0.262055 501 0.1855 10 10) SR_B2_p50 <= 0.052931 399 0.0527 10 20) SR_B4_p25 <= 0.036362 398 0.0498 10 40) SR_B5_p25 <= 0.112950 3 0.4800 10 * 41) SR_B5_p25 > 0.112950 276 0.0000 10 * 21) SR_B4_p25 > 0.036362 398 0.0498 10 42) SR_B6_p75 <= 0.153953 119 0.1467 10 84) SR_B3_p75 <= 0.061718 16 0.3457 10 168) SR_B7_p75 <= 0.082549 2 0.5000 10 * 169) SR_B7_p75 > 0.082549 8 0.0000 10 * 85) SR_B3_p75 > 0.061718 16 0.3457 10 170) SR_B2_mean <= 0.033476 2 0.0000 50 * 171) SR_B2_mean > 0.033476 4 0.2778 10 * 43) SR_B6_p75 > 0.153953 119 0.1467 10 86) SR_B2_p75 <= 0.041821 103 0.1011 10 172) SR_B3_p50 <= 0.058191 47 0.1979 10 344) SR_B4_p25 <= 0.036417 1 0.0000 50 * 345) SR_B4_p25 > 0.036417 40 0.1027 10 690) SR_B4_mean <= 0.050758 31 0.0000 10 * 691) SR_B4_mean > 0.050758 39 0.0707 10 1382) SR_B4_p90 <= 0.070270 8 0.4062 10 2764) SR_B6_mean <= 0.141859 1 0.0000 30 * 2765) SR_B6_mean > 0.141859 6 0.0000 10 * 1383) SR_B4_p90 > 0.070270 1 0.0000 50 * 173) SR_B3_p50 > 0.058191 47 0.1979 10 346) SR_B3_mean <= 0.055139 3 0.3750 30 * 347) SR_B3_mean > 0.055139 4 0.0000 10 * 87) SR_B2_p75 > 0.041821 103 0.1011 10 174) SR_B6_stdDev <= 0.054746 53 0.0000 10 * 175) SR_B6_stdDev > 0.054746 3 0.3200 10 * 11) SR_B2_p50 > 0.052931 1 0.0000 50 * 3) SR_B7_p90 > 0.138016 641 0.3244 10 6) SR_B4_p90 <= 0.094518 140 0.5437 50 12) SR_B5_p75 <= 0.282714 87 0.4691 10 24) SR_B7_mean <= 0.126061 61 0.5212 10 48) SR_B4_p10 <= 0.020124 9 0.0000 10 * 49) SR_B4_p10 > 0.020124 50 0.5085 10 98) SR_B6_p90 <= 0.191242 41 0.5302 50 196) SR_B5_p25 <= 0.162732 7 0.0000 50 * 197) SR_B5_p25 > 0.162732 3 0.3750 10 * 99) SR_B6_p90 > 0.191242 41 0.5302 50 198) SR_B6_p25 <= 0.137865 31 0.5215 10 396) SR_B6_p50 <= 0.164011 17 0.3018 10 792) SR_B7_mean <= 0.101803 2 0.0000 10 * 793) SR_B7_mean > 0.101803 3 0.0000 50 * 397) SR_B6_p50 > 0.164011 17 0.3018 10 794) SR_B3_p25 <= 0.061167 11 0.0000 10 * 795) SR_B3_p25 > 0.061167 1 0.0000 50 * 199) SR_B6_p25 > 0.137865 31 0.5215 10 398) SR_B4_p75 <= 0.064488 1 0.0000 30 * 399) SR_B4_p75 > 0.064488 14 0.4938 50 798) SR_B4_p50 <= 0.071143 13 0.3750 50 1596) SR_B7_p25 <= 0.084666 1 0.0000 10 * 1597) SR_B7_p25 > 0.084666 11 0.2449 50 3194) SR_B6_p75 <= 0.201844 9 0.0000 50 * 3195) SR_B6_p75 > 0.201844 1 0.0000 10 * 799) SR_B4_p50 > 0.071143 2 0.0000 10 * 25) SR_B7_mean > 0.126061 11 0.0000 50 * 13) SR_B5_p75 > 0.282714 87 0.4691 10 26) SR_B5_p75 <= 0.414893 26 0.1166 10 52) SR_B5_p50 <= 0.238989 1 0.0000 50 * 53) SR_B5_p50 > 0.238989 25 0.0799 10 106) SR_B3_p50 <= 0.066592 17 0.0000 10 * 107) SR_B3_p50 > 0.066592 24 0.0416 10 214) SR_B3_mean <= 0.063325 1 0.0000 50 * 215) SR_B3_mean > 0.063325 6 0.0000 10 * 27) SR_B5_p75 > 0.414893 1 0.0000 30 * 7) SR_B4_p90 > 0.094518 140 0.5437 50 14) SR_B2_p25 <= 0.041450 53 0.3717 50 28) SR_B5_p75 <= 0.341757 26 0.5429 50 56) SR_B4_p50 <= 0.084488 24 0.4429 50 112) SR_B5_p25 <= 0.151235 15 0.5714 50 224) SR_B3_mean <= 0.050912 1 0.0000 10 * 225) SR_B3_mean > 0.050912 5 0.0000 50 * 113) SR_B5_p25 > 0.151235 15 0.5714 50 226) SR_B3_p75 <= 0.076031 3 0.5000 30 * 227) SR_B3_p75 > 0.076031 9 0.5694 10 454) SR_B3_mean <= 0.070985 4 0.0000 10 * 455) SR_B3_mean > 0.070985 2 0.5000 10 * 57) SR_B4_p50 > 0.084488 9 0.0000 50 * 29) SR_B5_p75 > 0.341757 2 0.0000 40 * 15) SR_B2_p25 > 0.041450 53 0.3717 50 30) SR_B7_mean <= 0.198066 27 0.1446 50 60) SR_B5_p10 <= 0.164499 24 0.0000 50 * 61) SR_B5_p10 > 0.164499 2 0.5000 10 * 31) SR_B7_mean > 0.198066 1 0.0000 10 *
      • 1:1) root 614 9999 9999 (21.503702335033484) 2) SR_B4_p50 <= 0.067396 614 0.3697 10 4) SR_B5_p25 <= 0.120210 515 0.2242 10 8) SR_B3_mean <= 0.046532 39 0.6244 40 16) SR_B5_mean <= 0.070925 7 0.0000 50 * 17) SR_B5_mean > 0.070925 21 0.5350 10 34) SR_B5_stdDev <= 0.073604 7 0.0000 10 * 35) SR_B5_stdDev > 0.073604 14 0.3806 10 70) SR_B7_stdDev <= 0.024035 3 0.3750 50 * 71) SR_B7_stdDev > 0.024035 4 0.3200 10 * 9) SR_B3_mean > 0.046532 39 0.6244 40 18) SR_B7_p50 <= 0.069218 1 0.0000 10 * 19) SR_B7_p50 > 0.069218 18 0.1284 40 38) SR_B7_stdDev <= 0.029688 3 0.3200 40 * 39) SR_B7_stdDev > 0.029688 14 0.0000 40 * 5) SR_B5_p25 > 0.120210 515 0.2242 10 10) SR_B4_p25 <= 0.046758 476 0.1507 10 20) SR_B3_p90 <= 0.088874 395 0.0687 10 40) SR_B2_p90 <= 0.044186 394 0.0630 10 80) SR_B5_p50 <= 0.194281 310 0.0119 10 160) SR_B5_p50 <= 0.194102 18 0.1855 10 320) SR_B7_p25 <= 0.071693 13 0.0000 10 * 321) SR_B7_p25 > 0.071693 4 0.3457 10 * 161) SR_B5_p50 > 0.194102 1 0.0000 40 * 81) SR_B5_p50 > 0.194281 292 0.0000 10 * 41) SR_B2_p90 > 0.044186 394 0.0630 10 82) SR_B7_p50 <= 0.087602 84 0.2209 10 164) SR_B7_p90 <= 0.113582 20 0.4444 10 328) SR_B4_p25 <= 0.045637 12 0.0000 10 * 329) SR_B4_p25 > 0.045637 1 0.0000 40 * 165) SR_B7_p90 > 0.113582 20 0.4444 10 330) SR_B5_mean <= 0.219754 2 0.0000 40 * 331) SR_B5_mean > 0.219754 7 0.6633 30 662) SR_B4_p25 <= 0.031014 2 0.0000 10 * 663) SR_B4_p25 > 0.031014 3 0.0000 30 * 83) SR_B7_p50 > 0.087602 84 0.2209 10 166) SR_B6_p90 <= 0.164774 64 0.1212 10 332) SR_B4_p75 <= 0.059435 4 0.3200 10 * 333) SR_B4_p75 > 0.059435 1 0.0000 40 * 167) SR_B6_p90 > 0.164774 64 0.1212 10 334) SR_B4_p25 <= 0.041890 25 0.0000 10 * 335) SR_B4_p25 > 0.041890 59 0.0932 10 670) SR_B4_p50 <= 0.049143 1 0.0000 40 * 671) SR_B4_p50 > 0.049143 34 0.1576 10 1342) SR_B2_p50 <= 0.030911 1 0.0000 40 * 1343) SR_B2_p50 > 0.030911 33 0.1014 10 2686) SR_B7_p25 <= 0.080789 18 0.0000 10 * 2687) SR_B7_p25 > 0.080789 32 0.0701 10 5374) SR_B3_stdDev <= 0.024966 14 0.1800 10 10748) SR_B4_p25 <= 0.043753 6 0.0000 10 * 10749) SR_B4_p25 > 0.043753 13 0.0997 10 21498) SR_B2_p75 <= 0.041698 1 0.0000 40 * 21499) SR_B2_p75 > 0.041698 6 0.0000 10 * 5375) SR_B3_stdDev > 0.024966 1 0.0000 40 * 21) SR_B3_p90 > 0.088874 1 0.0000 30 * 11) SR_B4_p25 > 0.046758 476 0.1507 10 22) SR_B7_p75 <= 0.135706 81 0.4494 10 44) SR_B2_stdDev <= 0.013041 60 0.5120 10 88) SR_B5_p25 <= 0.158717 2 0.3750 40 * 89) SR_B5_p25 > 0.158717 20 0.3223 10 178) SR_B2_stdDev <= 0.008001 1 0.0000 30 * 179) SR_B2_stdDev > 0.008001 17 0.0000 10 * 45) SR_B2_stdDev > 0.013041 60 0.5120 10 90) SR_B4_p90 <= 0.073584 40 0.4966 40 180) SR_B3_p90 <= 0.071329 3 0.4082 40 * 181) SR_B3_p90 > 0.071329 12 0.0000 10 * 91) SR_B4_p90 > 0.073584 40 0.4966 40 182) SR_B3_p50 <= 0.060205 3 0.0000 10 * 183) SR_B3_p50 > 0.060205 25 0.4050 40 366) SR_B4_p75 <= 0.073274 22 0.3200 40 732) SR_B5_p25 <= 0.203384 13 0.0000 40 * 733) SR_B5_p25 > 0.203384 1 0.0000 10 * 367) SR_B4_p75 > 0.073274 22 0.3200 40 734) SR_B4_p25 <= 0.053646 3 0.4444 40 * 735) SR_B4_p25 > 0.053646 5 0.0000 10 * 23) SR_B7_p75 > 0.135706 81 0.4494 10 46) SR_B6_p90 <= 0.243795 17 0.0000 10 * 47) SR_B6_p90 > 0.243795 4 0.3200 10 * 3) SR_B4_p50 > 0.067396 614 0.3697 10 6) SR_B5_p75 <= 0.256239 99 0.4232 40 12) SR_B4_p90 <= 0.080789 3 0.4800 10 * 13) SR_B4_p90 > 0.080789 57 0.1732 40 26) SR_B3_stdDev <= 0.011145 2 0.0000 10 * 27) SR_B3_stdDev > 0.011145 54 0.1257 40 54) SR_B2_p25 <= 0.024716 1 0.0000 10 * 55) SR_B2_p25 > 0.024716 52 0.0877 40 110) SR_B3_p50 <= 0.059916 1 0.0000 10 * 111) SR_B3_p50 > 0.059916 51 0.0673 40 222) SR_B5_p25 <= 0.191916 50 0.0460 40 444) SR_B3_p90 <= 0.083601 46 0.0253 40 888) SR_B2_p50 <= 0.048765 9 0.0000 40 * 889) SR_B2_p50 > 0.048765 2 0.3750 40 * 445) SR_B3_p90 > 0.083601 35 0.0000 40 * 223) SR_B5_p25 > 0.191916 4 0.2449 40 * 7) SR_B5_p75 > 0.256239 99 0.4232 40 14) SR_B4_p90 <= 0.096120 42 0.5918 40 28) SR_B5_p90 <= 0.297585 19 0.3893 10 56) SR_B5_p10 <= 0.086413 1 0.0000 10 * 57) SR_B5_p10 > 0.086413 4 0.2188 40 * 29) SR_B5_p90 > 0.297585 19 0.3893 10 58) SR_B6_p75 <= 0.203858 14 0.1472 10 116) SR_B6_p50 <= 0.179624 3 0.0000 10 * 117) SR_B6_p50 > 0.179624 2 0.0000 40 * 59) SR_B6_p75 > 0.203858 9 0.0000 10 * 15) SR_B4_p90 > 0.096120 42 0.5918 40 30) SR_B3_p75 <= 0.106254 23 0.4861 40 60) SR_B2_stdDev <= 0.014488 2 0.0000 30 * 61) SR_B2_stdDev > 0.014488 14 0.6667 40 122) SR_B6_p90 <= 0.205845 1 0.0000 10 * 123) SR_B6_p90 > 0.205845 12 0.6016 40 246) SR_B5_p10 <= 0.179101 11 0.5408 40 492) SR_B2_p90 <= 0.074725 6 0.0000 40 * 493) SR_B2_p90 > 0.074725 2 0.5000 10 * 247) SR_B5_p10 > 0.179101 3 0.6250 30 * 31) SR_B3_p75 > 0.106254 9 0.0000 40 *
      • 2:1) root 638 9999 9999 (22.672185793313957) 2) SR_B2_p25 <= 0.035923 638 0.3686 10 4) SR_B5_p50 <= 0.201177 543 0.2487 10 8) SR_B3_p50 <= 0.028140 8 0.0000 50 * 9) SR_B3_p50 > 0.028140 90 0.5829 10 18) SR_B3_stdDev <= 0.024209 82 0.4951 10 36) SR_B3_p75 <= 0.068840 53 0.4246 10 72) SR_B3_p10 <= 0.030106 46 0.3265 10 144) SR_B4_p10 <= 0.030065 31 0.1172 10 288) SR_B5_p75 <= 0.133651 1 0.0000 40 * 289) SR_B5_p75 > 0.133651 28 0.0000 10 * 145) SR_B4_p10 > 0.030065 2 0.4444 40 * 73) SR_B3_p10 > 0.030106 46 0.3265 10 146) SR_B7_p50 <= 0.099956 15 0.4992 10 292) SR_B3_mean <= 0.051833 11 0.4654 40 584) SR_B2_p25 <= 0.022014 1 0.0000 40 * 585) SR_B2_p25 > 0.022014 5 0.0000 10 * 293) SR_B3_mean > 0.051833 5 0.0000 40 * 147) SR_B7_p50 > 0.099956 4 0.0000 10 * 37) SR_B3_p75 > 0.068840 53 0.4246 10 74) SR_B2_p90 <= 0.052216 3 0.4444 40 * 75) SR_B2_p90 > 0.052216 4 0.0000 40 * 19) SR_B3_stdDev > 0.024209 82 0.4951 10 38) SR_B5_p50 <= 0.190019 29 0.4055 40 76) SR_B2_stdDev <= 0.015168 3 0.0000 10 * 77) SR_B2_stdDev > 0.015168 20 0.2509 40 154) SR_B4_stdDev <= 0.047322 14 0.0000 40 * 155) SR_B4_stdDev > 0.047322 3 0.4444 40 * 39) SR_B5_p50 > 0.190019 29 0.4055 40 78) SR_B6_p10 <= 0.056506 1 0.0000 40 * 79) SR_B6_p10 > 0.056506 9 0.4444 10 158) SR_B4_stdDev <= 0.030619 6 0.0000 10 * 159) SR_B4_stdDev > 0.030619 2 0.0000 40 * 5) SR_B5_p50 > 0.201177 543 0.2487 10 10) SR_B4_mean <= 0.052069 453 0.1231 10 20) SR_B3_p75 <= 0.061979 367 0.0439 10 40) SR_B3_p10 <= 0.042481 312 0.0081 10 80) SR_B7_p50 <= 0.090751 284 0.0000 10 * 81) SR_B7_p50 > 0.090751 311 0.0041 10 162) SR_B4_p25 <= 0.036198 27 0.0487 10 324) SR_B7_p50 <= 0.090977 1 0.0000 40 * 325) SR_B7_p50 > 0.090977 6 0.0000 10 * 163) SR_B4_p25 > 0.036198 20 0.0000 10 * 41) SR_B3_p10 > 0.042481 1 0.0000 30 * 21) SR_B3_p75 > 0.061979 367 0.0439 10 42) SR_B6_p90 <= 0.160223 3 0.4444 40 * 43) SR_B6_p90 > 0.160223 55 0.2234 10 86) SR_B5_mean <= 0.224323 52 0.1591 10 172) SR_B7_mean <= 0.091591 18 0.2706 10 344) SR_B6_p90 <= 0.175574 5 0.0000 10 * 345) SR_B6_p90 > 0.175574 11 0.4861 10 690) SR_B5_p75 <= 0.263925 3 0.4444 10 * 691) SR_B5_p75 > 0.263925 3 0.0000 40 * 173) SR_B7_mean > 0.091591 7 0.0000 10 * 87) SR_B5_mean > 0.224323 52 0.1591 10 174) SR_B6_p75 <= 0.188534 24 0.0000 10 * 175) SR_B6_p75 > 0.188534 34 0.0754 10 350) SR_B6_mean <= 0.162453 4 0.4800 10 * 351) SR_B6_mean > 0.162453 6 0.0000 10 * 11) SR_B4_mean > 0.052069 453 0.1231 10 22) SR_B5_p25 <= 0.169298 86 0.4095 10 44) SR_B4_stdDev <= 0.026968 26 0.4996 40 88) SR_B5_p75 <= 0.292222 17 0.4527 10 176) SR_B3_p90 <= 0.070435 2 0.0000 40 * 177) SR_B3_p90 > 0.070435 14 0.2550 10 354) SR_B7_mean <= 0.123760 11 0.0000 10 * 355) SR_B7_mean > 0.123760 1 0.0000 40 * 89) SR_B5_p75 > 0.292222 3 0.0000 40 * 45) SR_B4_stdDev > 0.026968 26 0.4996 40 90) SR_B5_p50 <= 0.214782 3 0.4444 40 * 91) SR_B5_p50 > 0.214782 6 0.0000 40 * 23) SR_B5_p25 > 0.169298 86 0.4095 10 46) SR_B6_p50 <= 0.232933 60 0.2775 10 92) SR_B2_mean <= 0.033293 57 0.2134 10 184) SR_B7_stdDev <= 0.020312 1 0.0000 10 * 185) SR_B7_stdDev > 0.020312 4 0.0000 40 * 93) SR_B2_mean > 0.033293 57 0.2134 10 186) SR_B4_p50 <= 0.052409 1 0.0000 30 * 187) SR_B4_p50 > 0.052409 52 0.1205 10 374) SR_B5_stdDev <= 0.087812 51 0.0756 10 748) SR_B4_p90 <= 0.068716 4 0.3750 10 * 749) SR_B4_p90 > 0.068716 48 0.0533 10 1498) SR_B7_p10 <= 0.049714 44 0.0286 10 2996) SR_B2_p90 <= 0.052106 2 0.5000 10 * 2997) SR_B2_p90 > 0.052106 5 0.0000 10 * 1499) SR_B7_p10 > 0.049714 37 0.0000 10 * 375) SR_B5_stdDev > 0.087812 3 0.3750 10 * 47) SR_B6_p50 > 0.232933 3 0.0000 30 * 3) SR_B2_p25 > 0.035923 638 0.3686 10 6) SR_B2_p25 <= 0.043870 95 0.4382 40 12) SR_B3_stdDev <= 0.026167 63 0.5204 40 24) SR_B6_p75 <= 0.175478 44 0.5445 10 48) SR_B5_p25 <= 0.168170 11 0.3047 40 96) SR_B5_p50 <= 0.212816 10 0.2311 40 192) SR_B6_stdDev <= 0.027686 1 0.0000 10 * 193) SR_B6_stdDev > 0.027686 8 0.0000 40 * 97) SR_B5_p50 > 0.212816 1 0.0000 10 * 49) SR_B5_p25 > 0.168170 1 0.0000 10 * 25) SR_B6_p75 > 0.175478 44 0.5445 10 50) SR_B4_p50 <= 0.082067 33 0.5136 10 100) SR_B5_p10 <= 0.185206 28 0.4197 10 200) SR_B4_p10 <= 0.062460 26 0.3200 10 400) SR_B6_p50 <= 0.163990 25 0.2907 10 800) SR_B6_stdDev <= 0.043728 4 0.0000 40 * 801) SR_B6_stdDev > 0.043728 1 0.0000 10 * 401) SR_B6_p50 > 0.163990 25 0.2907 10 802) SR_B4_p25 <= 0.061113 13 0.0000 10 * 803) SR_B4_p25 > 0.061113 20 0.1327 10 1606) SR_B7_p90 <= 0.158050 3 0.4444 40 * 1607) SR_B7_p90 > 0.158050 4 0.0000 10 * 201) SR_B4_p10 > 0.062460 1 0.0000 40 * 101) SR_B5_p10 > 0.185206 2 0.4444 30 * 51) SR_B4_p50 > 0.082067 5 0.0000 40 * 13) SR_B3_stdDev > 0.026167 63 0.5204 40 26) SR_B5_p90 <= 0.353561 16 0.0000 40 * 27) SR_B5_p90 > 0.353561 3 0.6250 10 * 7) SR_B2_p25 > 0.043870 95 0.4382 40 14) SR_B7_p75 <= 0.252856 32 0.1752 40 28) SR_B5_p25 <= 0.226291 30 0.0776 40 56) SR_B4_mean <= 0.078718 29 0.0400 40 112) SR_B6_p90 <= 0.212968 4 0.0000 40 * 113) SR_B6_p90 > 0.212968 1 0.0000 30 * 57) SR_B4_mean > 0.078718 24 0.0000 40 * 29) SR_B5_p25 > 0.226291 1 0.0000 10 * 15) SR_B7_p75 > 0.252856 2 0.4444 10 *
      • 3:1) root 615 9999 9999 (20.118541236119068) 2) SR_B7_p50 <= 0.134132 615 0.3738 10 4) SR_B5_mean <= 0.192392 565 0.2932 10 8) SR_B6_p90 <= 0.094566 11 0.0000 50 * 9) SR_B6_p90 > 0.094566 103 0.5945 40 18) SR_B4_p75 <= 0.061807 92 0.4998 40 36) SR_B4_p75 <= 0.052986 39 0.2741 10 72) SR_B3_stdDev <= 0.017269 21 0.0605 10 144) SR_B5_mean <= 0.160550 1 0.0000 40 * 145) SR_B5_mean > 0.160550 8 0.0000 10 * 73) SR_B3_stdDev > 0.017269 12 0.0000 10 * 37) SR_B4_p75 > 0.052986 39 0.2741 10 74) SR_B7_mean <= 0.078075 2 0.0000 40 * 75) SR_B7_mean > 0.078075 18 0.4281 10 150) SR_B7_p75 <= 0.100135 4 0.2778 40 * 151) SR_B7_p75 > 0.100135 16 0.3550 10 302) SR_B7_p90 <= 0.129533 10 0.0000 10 * 303) SR_B7_p90 > 0.129533 2 0.5000 10 * 19) SR_B4_p75 > 0.061807 92 0.4998 40 38) SR_B3_p50 <= 0.054004 4 0.0000 10 * 39) SR_B3_p50 > 0.054004 53 0.3904 40 78) SR_B5_p25 <= 0.120024 17 0.0000 40 * 79) SR_B5_p25 > 0.120024 49 0.3484 40 158) SR_B3_p10 <= 0.024084 4 0.0000 10 * 159) SR_B3_p10 > 0.024084 32 0.4592 40 318) SR_B3_p25 <= 0.042248 2 0.0000 10 * 319) SR_B3_p25 > 0.042248 28 0.3898 40 638) SR_B2_p90 <= 0.060672 26 0.2975 40 1276) SR_B5_p25 <= 0.127401 3 0.4800 10 * 1277) SR_B5_p25 > 0.127401 16 0.0000 40 * 639) SR_B2_p90 > 0.060672 26 0.2975 40 1278) SR_B2_p50 <= 0.049810 3 0.0000 10 * 1279) SR_B2_p50 > 0.049810 4 0.0000 40 * 5) SR_B5_mean > 0.192392 565 0.2932 10 10) SR_B5_p90 <= 0.262440 462 0.1444 10 20) SR_B4_p25 <= 0.045259 13 0.0000 10 * 21) SR_B4_p25 > 0.045259 29 0.4352 10 42) SR_B7_p50 <= 0.125352 16 0.4234 40 84) SR_B4_mean <= 0.063253 14 0.3200 40 168) SR_B3_p75 <= 0.068386 2 0.0000 40 * 169) SR_B3_p75 > 0.068386 3 0.0000 10 * 85) SR_B4_mean > 0.063253 14 0.3200 40 170) SR_B6_p25 <= 0.154743 7 0.0000 40 * 171) SR_B6_p25 > 0.154743 2 0.5000 10 * 43) SR_B7_p50 > 0.125352 2 0.0000 10 * 11) SR_B5_p90 > 0.262440 462 0.1444 10 22) SR_B3_p25 <= 0.047995 433 0.1135 10 44) SR_B5_p50 <= 0.234521 332 0.0303 10 88) SR_B5_p25 <= 0.113830 1 0.0000 50 * 89) SR_B5_p25 > 0.113830 83 0.1139 10 178) SR_B6_stdDev <= 0.018591 1 0.0000 40 * 179) SR_B6_stdDev > 0.018591 82 0.1004 10 358) SR_B5_p50 <= 0.234273 81 0.0874 10 716) SR_B7_p90 <= 0.154386 80 0.0458 10 1432) SR_B3_p50 <= 0.061896 77 0.0315 10 2864) SR_B3_mean <= 0.054304 64 0.0000 10 * 2865) SR_B3_mean > 0.054304 74 0.0169 10 5730) SR_B2_p25 <= 0.026064 2 0.5000 10 * 5731) SR_B2_p25 > 0.026064 8 0.0000 10 * 1433) SR_B3_p50 > 0.061896 3 0.2188 10 * 717) SR_B7_p90 > 0.154386 3 0.4444 10 * 359) SR_B5_p50 > 0.234273 1 0.0000 40 * 45) SR_B5_p50 > 0.234521 249 0.0000 10 * 23) SR_B3_p25 > 0.047995 433 0.1135 10 46) SR_B5_p50 <= 0.333294 101 0.3319 10 92) SR_B4_stdDev <= 0.018324 98 0.3010 10 184) SR_B5_stdDev <= 0.057123 41 0.0263 10 368) SR_B6_p50 <= 0.174186 10 0.0000 10 * 369) SR_B6_p50 > 0.174186 1 0.0000 30 * 185) SR_B5_stdDev > 0.057123 30 0.0000 10 * 93) SR_B4_stdDev > 0.018324 98 0.3010 10 186) SR_B7_p75 <= 0.124252 57 0.4612 10 372) SR_B7_p50 <= 0.087526 2 0.0000 30 * 373) SR_B7_p50 > 0.087526 25 0.6113 10 746) SR_B5_p75 <= 0.273371 23 0.5208 10 1492) SR_B7_p25 <= 0.088723 8 0.3911 40 2984) SR_B7_p25 <= 0.074601 1 0.0000 10 * 2985) SR_B7_p25 > 0.074601 5 0.0000 40 * 1493) SR_B7_p25 > 0.088723 2 0.0000 10 * 747) SR_B5_p75 > 0.273371 23 0.5208 10 1494) SR_B4_p25 <= 0.049356 15 0.3950 10 2988) SR_B4_stdDev <= 0.020392 1 0.0000 40 * 2989) SR_B4_stdDev > 0.020392 12 0.2400 10 5978) SR_B5_p10 <= 0.162244 8 0.0000 10 * 5979) SR_B5_p10 > 0.162244 3 0.4444 10 * 1495) SR_B4_p25 > 0.049356 3 0.4800 40 * 187) SR_B7_p75 > 0.124252 57 0.4612 10 374) SR_B2_p25 <= 0.027377 1 0.0000 40 * 375) SR_B2_p25 > 0.027377 32 0.2408 10 750) SR_B7_stdDev <= 0.047005 31 0.1866 10 1500) SR_B4_p10 <= 0.003734 1 0.0000 40 * 1501) SR_B4_p10 > 0.003734 30 0.1557 10 3002) SR_B4_p90 <= 0.094731 29 0.1219 10 6004) SR_B5_p50 <= 0.283478 20 0.0000 10 * 6005) SR_B5_p50 > 0.283478 3 0.4444 10 * 3003) SR_B4_p90 > 0.094731 29 0.1219 10 6006) SR_B5_p25 <= 0.182381 2 0.0000 40 * 6007) SR_B5_p25 > 0.182381 4 0.0000 10 * 751) SR_B7_stdDev > 0.047005 1 0.0000 40 * 47) SR_B5_p50 > 0.333294 3 0.3750 30 * 3) SR_B7_p50 > 0.134132 615 0.3738 10 6) SR_B5_p75 <= 0.342101 50 0.3399 40 12) SR_B2_p90 <= 0.062054 47 0.2606 40 24) SR_B2_mean <= 0.040484 15 0.5307 40 48) SR_B6_stdDev <= 0.066031 10 0.3629 40 96) SR_B2_stdDev <= 0.013958 1 0.0000 10 * 97) SR_B2_stdDev > 0.013958 7 0.0000 40 * 49) SR_B6_stdDev > 0.066031 2 0.0000 30 * 25) SR_B2_mean > 0.040484 15 0.5307 40 50) SR_B2_p25 <= 0.044200 4 0.0000 10 * 51) SR_B2_p25 > 0.044200 1 0.0000 40 * 13) SR_B2_p90 > 0.062054 32 0.0000 40 * 7) SR_B5_p75 > 0.342101 3 0.4800 30 *
      • 4:1) root 621 9999 9999 (22.7922741185705) 2) SR_B2_p50 <= 0.043650 621 0.3551 10 4) SR_B4_p75 <= 0.064990 516 0.2157 10 8) SR_B7_p75 <= 0.044750 430 0.1299 10 16) SR_B2_p90 <= 0.017676 1 0.0000 10 * 17) SR_B2_p90 > 0.017676 9 0.0000 50 * 9) SR_B7_p75 > 0.044750 430 0.1299 10 18) SR_B5_p90 <= 0.246380 420 0.0944 10 36) SR_B3_mean <= 0.054206 35 0.3599 10 72) SR_B3_p10 <= 0.030381 31 0.2550 10 144) SR_B5_p50 <= 0.167036 4 0.4444 10 * 145) SR_B5_p50 > 0.167036 19 0.0000 10 * 73) SR_B3_p10 > 0.030381 31 0.2550 10 146) SR_B2_p90 <= 0.040212 3 0.0000 40 * 147) SR_B2_p90 > 0.040212 8 0.4970 10 294) SR_B3_mean <= 0.046077 1 0.0000 40 * 295) SR_B3_mean > 0.046077 4 0.0000 10 * 37) SR_B3_mean > 0.054206 4 0.2188 40 * 19) SR_B5_p90 > 0.246380 420 0.0944 10 38) SR_B3_p25 <= 0.051453 385 0.0552 10 76) SR_B4_p75 <= 0.052477 371 0.0372 10 152) SR_B6_stdDev <= 0.026120 255 0.0050 10 304) SR_B4_p50 <= 0.022874 2 0.5000 10 * 305) SR_B4_p50 > 0.022874 24 0.0000 10 * 153) SR_B6_stdDev > 0.026120 229 0.0000 10 * 77) SR_B4_p75 > 0.052477 371 0.0372 10 154) SR_B6_p10 <= 0.030148 1 0.0000 40 * 155) SR_B6_p10 > 0.030148 116 0.1062 10 310) SR_B5_mean <= 0.218768 115 0.0966 10 620) SR_B2_p10 <= 0.027095 58 0.1923 10 1240) SR_B4_p90 <= 0.076911 54 0.1172 10 2480) SR_B4_p50 <= 0.049171 52 0.0973 10 4960) SR_B3_mean <= 0.053074 24 0.2130 10 9920) SR_B3_p90 <= 0.073281 18 0.0000 10 * 9921) SR_B3_p90 > 0.073281 2 0.5000 10 * 4961) SR_B3_mean > 0.053074 4 0.4800 40 * 2481) SR_B4_p50 > 0.049171 28 0.0000 10 * 1241) SR_B4_p90 > 0.076911 2 0.5000 10 * 621) SR_B2_p10 > 0.027095 4 0.6111 30 * 311) SR_B5_mean > 0.218768 57 0.0000 10 * 39) SR_B3_p25 > 0.051453 385 0.0552 10 78) SR_B5_stdDev <= 0.094885 14 0.4600 10 156) SR_B4_p90 <= 0.076705 13 0.3045 10 312) SR_B7_mean <= 0.103059 10 0.0000 10 * 313) SR_B7_mean > 0.103059 2 0.4444 40 * 157) SR_B4_p90 > 0.076705 1 0.0000 30 * 79) SR_B5_stdDev > 0.094885 1 0.0000 30 * 5) SR_B4_p75 > 0.064990 516 0.2157 10 10) SR_B5_p90 <= 0.254080 86 0.5220 10 20) SR_B6_p25 <= 0.141399 22 0.3820 40 40) SR_B3_p90 <= 0.076361 20 0.2706 40