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The documentation states that influence trimming can be used "to reduce the computation time for boosted models with substantially losing accuracy". By default, the weight_trim_rate parameter is 0.95. After disabling influence training by changing that parameter to 0, I actually achieve a large speed-up. When using a dataset with 262144 samples, I achieve a 5x speed-up. When using a dataset ten times larger, I achieve a 3x speed-up. This seems to be the opposite of the expected behavior. Can anyone explain why this might be happening? Thanks!

The documentation states that influence trimming can be used "to reduce the computation time for boosted models with substantially losing accuracy". By default, the weight_trim_rate parameter is 0.95. After disabling influence training by changing that parameter to 0, I actually achieve a large speed-up. When using a dataset with 262144 samples, I achieve a 5x speed-up. When using a dataset ten times larger, I achieve a 3x speed-up. This seems to be the opposite of the expected behavior. Can anyone explain why this might be happening? Thanks!

100 weak classifiers with a max depth of 1              
Trim    Accuracy    MSE     Training Time   Percent Speedup
0       95.03      3.989   10.607   
0.6   7.88      86.77     1.252          8.472044728
0.7  15.76      78.21     2.319          4.573954291
0.8  33.35      57.73    52.972          0.200237862
0.9  94.68       4.89    52.484          0.202099688
0.95    94.94       4.189   52.31           0.202771937
0.99    95.03       3.99    47.026          0.225556075
0.999   95.02      3.985   44.432          0.238724343

The documentation states that influence trimming can be used "to reduce the computation time for boosted models with substantially losing accuracy". By default, the weight_trim_rate parameter is 0.95. After disabling influence training by changing that parameter to 0, I actually achieve a large speed-up. When using a dataset with 262144 samples, I achieve a 5x speed-up. When using a dataset ten times larger, I achieve a 3x speed-up. This seems to be the opposite of the expected behavior. Can anyone explain why this might be happening? Thanks!

100 weak classifiers with a max depth of 1              
Trim    Accuracy    MSE     Training Time   Percent Speedup
0       95.03      3.989   10.607   
0.6    7.88       86.77     1.252          8.472044728
0.7   15.76       78.21     2.319          4.573954291
0.8   33.35       57.73    52.972          0.200237862
0.9   94.68        4.89    52.484          0.202099688
0.95    94.94       4.189   52.31           0.202771937
0.99    95.03       3.99    47.026          0.225556075
0.999   95.02      3.985   44.432          0.238724343

The documentation states that influence trimming can be used "to reduce the computation time for boosted models with substantially losing accuracy". By default, the weight_trim_rate parameter is 0.95. After disabling influence training by changing that parameter to 0, I actually achieve a large speed-up. When using a dataset with 262144 samples, I achieve a 5x speed-up. When using a dataset ten times larger, I achieve a 3x speed-up. This seems to be the opposite of the expected behavior. Can anyone explain why this might be happening? Thanks!

100 weak classifiers with a max depth of 1              
Trim    Accuracy    MSE     Training Time   Percent Speedup
0       95.03       3.989   10.607  
0.6      7.88       86.77     1.252          8.472044728
0.7     15.76       78.21     2.319          4.573954291
0.8     33.35       57.73    52.972          0.200237862
0.9     94.68        4.89    52.484          0.202099688
0.95    94.94        4.189   52.31           0.202771937
0.99    95.03        3.99    47.026          0.225556075
0.999   95.02       3.985   44.432         0.238724343

The documentation states that influence trimming can be used "to reduce the computation time for boosted models with substantially losing accuracy". By default, the weight_trim_rate parameter is 0.95. After disabling influence training by changing that parameter to 0, I actually achieve a large speed-up. When using a dataset with 262144 samples, I achieve a 5x speed-up. When using a dataset ten times larger, I achieve a 3x speed-up. This seems to be the opposite of the expected behavior. Can anyone explain why this might be happening? Thanks!

100 weak classifiers with a max depth of 1              
Trim    Accuracy    MSE     Training Time   Percent Speedup
0       95.03        3.989   10.607 
0.6      7.88       86.77     1.252          8.472044728
0.7     15.76       78.21     2.319          4.573954291
0.8     33.35       57.73    52.972          0.200237862
0.9     94.68        4.89    52.484          0.202099688
0.95    94.94        4.189   52.31           0.202771937
0.99    95.03        3.99    47.026          0.225556075
0.999   95.02        3.985   44.432         0.238724343

The documentation states that influence trimming can be used "to reduce the computation time for boosted models with substantially losing accuracy". By default, the weight_trim_rate parameter is 0.95. After disabling influence training by changing that parameter to 0, I actually achieve a large speed-up. When using a dataset with 262144 samples, I achieve a 5x speed-up. When using a dataset ten times larger, I achieve a 3x speed-up. This seems to be the opposite of the expected behavior. Can anyone explain why this might be happening? Thanks!

100 weak classifiers with a max depth of 1              
Trim    Accuracy    MSE     Training Time   Percent Speedup
0       95.03        3.989   10.607 
0.6      7.88       86.77     1.252          8.472044728
0.7     15.76       78.21     2.319          4.573954291
0.8     33.35       57.73    52.972          0.200237862
0.9     94.68        4.89    52.484          0.202099688
0.95    94.94        4.189   52.31           0.202771937
0.99    95.03        3.99    47.026          0.225556075
0.999   95.02        3.985   44.432          0.238724343