X,Y,Test or train,Raw prediction,Cumulative prediction,Logistic prediction
-124.108347,40.501172,train,7.852193709811683E-4,47.10736285358017,0.4776740228839262
-124.159253,40.79682,train,0.002379477991753544,56.59896825213561,0.7348381639750896
-124.120168,40.944258,train,9.060209561572658E-4,48.53867457668636,0.5134307615959616
-124.074129,40.032486,train,2.8288060674824285E-4,32.836256426196684,0.24781467669361368
-124.16703,40.757595,train,0.0016834642912071092,53.52044377276581,0.6622377901353853
-124.08796,40.882031,train,9.781987168030644E-4,49.33015866617144,0.5325504915284417
-124.190335,40.778722,train,0.0031051158959540365,59.45055938169589,0.7833811253230714
-124.158576,40.806696,train,0.0017744987399221914,54.32723975956391,0.6739151960825702
-124.051481,40.851155,train,6.593977643015096E-4,44.73463336695714,0.4343807922756199
-123.993529,41.513747,train,1.3996748832768182E-4,20.909840480889677,0.1401654720293265
-124.26144,40.584434,train,0.0018522652892242427,54.83097871182746,0.6832695165956019
-124.16433,40.845616,train,0.003784474348961896,62.83899336850278,0.815075907327562
-124.206324,40.567112,train,0.0011355432921241599,50.756613809675635,0.5694334510369359