mirror of https://github.com/01-edu/public.git
feat(training): updated expected result for ex05/q1
This commit is contained in:
parent
b19e6dd7d5
commit
1f9c0db5ad
|
@ -128,70 +128,66 @@ Having a 99% ROC AUC is not usual. The data set we used is easy to classify. On
|
|||
###### For question 1, are the scores outputted close to the scores below? Some of the algorithms use random steps (random sampling used by the `RandomForest`). I used `random_state = 43` for the Random Forest, the Decision Tree and the Gradient Boosting.
|
||||
|
||||
```console
|
||||
# Linear regression
|
||||
~~~
|
||||
Linear Regression
|
||||
|
||||
TRAIN
|
||||
r2 on the train set: 0.34823544284172625
|
||||
MAE on the train set: 0.533092001261455
|
||||
MSE on the train set: 0.5273648371379568
|
||||
r2 score: 0.6054131599242079
|
||||
MAE: 0.5330920012614552
|
||||
MSE: 0.5273648371379568
|
||||
|
||||
TEST
|
||||
r2 on the test set: 0.3551785428138914
|
||||
MAE on the test set: 0.5196420310323713
|
||||
MSE on the test set: 0.49761195027083804
|
||||
|
||||
|
||||
# SVM
|
||||
r2 score: 0.6128959462132963
|
||||
MAE: 0.5196420310323714
|
||||
MSE: 0.49761195027083804
|
||||
~~~
|
||||
SVM
|
||||
|
||||
TRAIN
|
||||
r2 on the train set: 0.6462366150965996
|
||||
MAE on the train set: 0.38356451633259875
|
||||
MSE on the train set: 0.33464478671339165
|
||||
r2 score: 0.749610858293664
|
||||
MAE: 0.3835645163325988
|
||||
MSE: 0.3346447867133917
|
||||
|
||||
TEST
|
||||
r2 on the test set: 0.6162644671183826
|
||||
MAE on the test set: 0.3897680598426786
|
||||
MSE on the test set: 0.3477101776543003
|
||||
|
||||
|
||||
# Decision Tree
|
||||
r2 score: 0.7295080649899683
|
||||
MAE: 0.38976805984267887
|
||||
MSE: 0.3477101776543005
|
||||
~~~
|
||||
Decision Tree
|
||||
|
||||
TRAIN
|
||||
r2 on the train set: 0.9999999999999488
|
||||
MAE on the train set: 1.3685733933909677e-08
|
||||
MSE on the train set: 6.842866883530944e-14
|
||||
r2 score: 1.0
|
||||
MAE: 4.221907539810565e-17
|
||||
MSE: 9.24499456646287e-32
|
||||
|
||||
TEST
|
||||
r2 on the test set: 0.6263651902480918
|
||||
MAE on the test set: 0.4383758696244002
|
||||
MSE on the test set: 0.4727017198871596
|
||||
|
||||
|
||||
# Random Forest
|
||||
r2 score: 0.6228217144931267
|
||||
MAE: 0.4403051356589147
|
||||
MSE: 0.4848526395290697
|
||||
~~~
|
||||
Random Forest
|
||||
|
||||
TRAIN
|
||||
r2 on the train set: 0.9705418471542886
|
||||
MAE on the train set: 0.11983836612191189
|
||||
MSE on the train set: 0.034538356420577995
|
||||
r2 score: 0.9741263135396302
|
||||
MAE: 0.12000198560508221
|
||||
MSE: 0.03458015083247723
|
||||
|
||||
TEST
|
||||
r2 on the test set: 0.7504673649554309
|
||||
MAE on the test set: 0.31889891600404635
|
||||
MSE on the test set: 0.24096164834441108
|
||||
|
||||
|
||||
# Gradient Boosting
|
||||
r2 score: 0.8119778189909694
|
||||
MAE: 0.3194169859011629
|
||||
MSE: 0.24169750554364758
|
||||
~~~
|
||||
Gradient Boosting
|
||||
|
||||
TRAIN
|
||||
r2 on the train set: 0.7395782392433273
|
||||
MAE on the train set: 0.35656543036682264
|
||||
MSE on the train set: 0.26167490389525294
|
||||
r2 score: 0.8042086499063386
|
||||
MAE: 0.35656543036682264
|
||||
MSE: 0.26167490389525294
|
||||
|
||||
TEST
|
||||
r2 on the test set: 0.7157456298013534
|
||||
MAE on the test set: 0.36455447680396397
|
||||
MSE on the test set: 0.27058170064218096
|
||||
|
||||
r2 score: 0.7895081234643192
|
||||
MAE: 0.36455447680396397
|
||||
MSE: 0.27058170064218096
|
||||
```
|
||||
|
||||
It is important to notice that the Decision Tree overfits very easily. It learns easily the training data but is not able to extrapolate on the test set. This algorithm is not used a lot because of its overfitting ability.
|
||||
|
|
Loading…
Reference in New Issue