Threshold table for eXtreme Gradient Boosting

This is the threshold page for the eXtreme Gradient Boosting classifier implemented in the server. The user may refer the threshold values from here according to their need. The below table exhibit the values of various parameters and their descritpions are as follows:
#Threshold: This is the threshold value for the probability score, above which the query is designated as IL-13 inducer, otherwise IL-13 non-inducer.
#Coverage: This is the model's ability to predict the true positives (IL-13 inducer).
#Positive Predictive Value (PPV): This exhibits the percentage of correctly predicted IL-13 inducers compared to the total number of cases predicted as IL-13 inducer.
#Error: This is the error rate for positive prediction calculated by 1-PPV.


ThresholdCoverage (Percentage)Positive Predictive Value (Percentage)Error (Percentage)
0.00100.009.1290.88
0.0197.029.8190.19
0.0292.3412.2087.80
0.0386.8114.9085.10
0.0485.1118.2381.77
0.0582.1321.5678.44
0.0678.3023.8076.20
0.0773.6225.5274.48
0.0871.0627.6572.35
0.0968.5128.6071.40
0.1064.6830.0469.96
0.1163.8332.1267.88
0.1263.4034.4165.59
0.1360.8534.8865.12
0.1459.1535.6464.36
0.1557.8736.9663.04
0.1655.3237.4662.54
0.1754.4739.0260.98
0.1853.1939.9460.06
0.1952.7742.3257.68
0.2051.9244.3655.64
0.2150.6444.9155.09
0.2249.3646.0353.97
0.2349.3648.7451.26
0.2448.9450.4449.56
0.2548.5152.0547.95
0.2648.0954.3345.67
0.2748.0955.6744.33
0.2846.3857.3742.63
0.2944.6858.3341.67
0.3042.1357.5642.44
0.3141.7059.0440.96
0.3240.8558.9041.10
0.3339.5758.4941.51
0.3439.1560.9339.07
0.3538.3060.8139.19
0.3637.4560.6939.31
0.3737.4564.2335.77
0.3837.0264.9335.07
0.3936.6066.1533.85
0.4036.1765.8934.11
0.4134.8967.7732.23
0.4234.4768.6431.36
0.4334.4769.2330.77
0.4434.0469.5730.43
0.4533.6269.9130.09
0.4632.7769.3730.63
0.4731.9269.4430.56
0.4831.9269.4430.56
0.4931.4970.4829.52
0.5030.6470.5929.41
0.5129.7972.1627.84
0.5229.7974.4725.53
0.5328.9475.5624.44
0.5428.5176.1423.86
0.5528.0976.7423.26
0.5627.2377.1122.89
0.5727.2379.0120.99
0.5826.3878.4821.52
0.5925.1179.7320.27
0.6025.1180.8219.18
0.6124.6880.5619.44
0.6224.6881.6918.31
0.6323.8381.1618.84
0.6422.9881.8218.18
0.6522.5581.5418.46
0.6621.2881.9718.03
0.6720.8581.6718.33
0.6820.8581.6718.33
0.6920.0081.0318.97
0.7019.5780.7019.30
0.7119.1580.3619.64
0.7218.7280.0020.00
0.7317.4580.3919.61
0.7416.1779.1720.83
0.7515.7578.7221.28
0.7614.8977.7822.22
0.7714.4777.2722.73
0.7813.6276.1923.81
0.7913.1975.6124.39
0.8012.7776.9223.08
0.8111.4977.1422.86
0.829.3673.3326.67
0.838.9472.4127.59
0.848.9475.0025.00
0.857.6672.0028.00
0.866.8169.5730.43
0.876.3868.1831.82
0.886.3868.1831.82
0.896.3871.4328.57
0.905.1175.0025.00
0.915.1180.0020.00
0.924.2683.3316.67
0.933.4088.8911.11
0.942.9887.5012.50
0.952.1383.3316.67
0.961.2875.0025.00
0.970.000.00100.00
0.980.000.000.00
0.990.000.000.00
1.000.000.000.00
==== Reference ====
  1. Jain S., Dhall A., Patiyal S. and Raghava G.P.S. (2022) IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides. Computers in Biology and Medicine, 2022: 05297.
  2. Jain S, Dhall A, Patiyal S, Raghava GPS. In Silico Tool for Identification, Designing, and Searching of IL13-Inducing Peptides in Antigens. Methods Mol Biol. 2023;2673:329-338.