I am using Random Forests, XGBoost and SVMs to classify whether the home team wins or the away team wins their bowl game (in college football). I trained the models on all the games during the season.

I've come across something that is a bit weird and can't explain. I calculated a prediction confidence by subtracting the class probabilities. The XGBoost confidence values are consistency higher than both Random Forests and SVM's. I've attached the image below.

I did some hyper-parameter tuning for all of my models and used the best parameters based on testing accuracy.

• Random Forest:
  • 700 trees
  • 15 variables randomly sampled (mtries)
  • minimum split criteria of 5 rows.
• XGBoost:
  • 0.5, Learn rate
  • gbtree as my booster
  • max depth of 6
• SVM:
  • RBF kernel
  • C (slack) of 1
  • 0.01, Sigma

I wasn't clear with my question: Why exactly does XGBoost prefer one class greatly to the other? In comparison to these other methods. I'm trying to figure out why my prediction confidences of a class are so high for XGboost.

[![First half][1]][1] [![Second half][2]][2] [1]: https://i.sstatic.net/WTCo5.png [2]: https://i.sstatic.net/aDyqz.png

I am using Random Forests, XGBoost and SVMs to classify whether the home team wins or the away team wins their bowl game (in college football). I trained the models on all the games during the season.

I've come across something that is a bit weird and can't explain. I calculated a prediction confidence by subtracting the class probabilities. The XGBoost confidence values are consistency higher than both Random Forests and SVM's. I've attached the image below.

I did some hyper-parameter tuning for all of my models and used the best parameters based on testing accuracy.

• Random Forest:
  • 700 trees
  • 15 variables randomly sampled (mtries)
  • minimum split criteria of 5 rows.
• XGBoost:
  • 0.5, Learn rate
  • gbtree as my booster
  • max depth of 6
• SVM:
  • RBF kernel
  • C (slack) of 1
  • 0.01, Sigma

I wasn't clear with my question: Why exactly does XGBoost prefer one class greatly to the other? In comparison to these other methods. I'm trying to figure out why my prediction confidences of a class are so high for XGboost.

I am using Random Forests, XGBoost and SVMs to classify whether the home team wins or the away team wins their bowl game (in college football). I trained the models on all the games during the season.

I've come across something that is a bit weird and can't explain. I calculated a prediction confidence by subtracting the class probabilities. The XGBoost confidence values are consistency higher than both Random Forests and SVM's. I've attached the image below.

I did some hyper-parameter tuning for all of my models and used the best parameters based on testing accuracy.

• Random Forest:
  • 700 trees
  • 15 variables randomly sampled (mtries)
  • minimum split criteria of 5 rows.
• XGBoost:
  • 0.5, Learn rate
  • gbtree as my booster
  • max depth of 6
• SVM:
  • RBF kernel
  • C (slack) of 1
  • 0.01, Sigma

[![First half][1]][1] [![Second half][2]][2] [1]: https://i.sstatic.net/WTCo5.png [2]: https://i.sstatic.net/aDyqz.png

I am using Random Forests, XGBoost and SVMs to classify whether the home team wins or the away team wins their bowl game (in college football). I trained the models on all the games during the season.

I've come across something that is a bit weird and can't explain. I calculated a prediction confidence by subtracting the class probabilities. The XGBoost confidence values are consistency higher than both Random Forests and SVM's. I've attached the image below.

I did some hyper-parameter tuning for all of my models and used the best parameters based on testing accuracy.

• Random Forest:
  • 700 trees
  • 15 variables randomly sampled (mtries)
  • minimum split criteria of 5 rows.
• XGBoost:
  • 0.5, Learn rate
  • gbtree as my booster
  • max depth of 6
• SVM:
  • RBF kernel
  • C (slack) of 1
  • 0.01, Sigma

I wasn't clear with my question: Why exactly does XGBoost prefer one class greatly to the other? In comparison to these other methods. I'm trying to figure out why my prediction confidences of a class are so high for XGboost.

[![First half][1]][1] [![Second half][2]][2] [1]: https://i.sstatic.net/WTCo5.png [2]: https://i.sstatic.net/aDyqz.png