The figure illustrated the feature importance of each substructure in the corresponding compound. The numbers were the value of feature importance
The figure illustrated the feature importance of each substructure in the corresponding compound. The numbers were the value of feature importance
python main.py
- The results in the result folder
. Train R2_Score: 0.9778 Test R2_Score: 0.8546
. Train RMSE: 0.0404 Test MSE: 0.1012
. Train MAE: 0.0247 Test MAE: 0.0665
feature sort: Features Importance
25 25 0.467576
10 10 0.055683
77 77 0.031899
71 71 0.030527
39 39 0.025558
.. ... ...
59 59 0.000000
3 3 0.000000
24 24 0.000000
29 29 0.000000
35 35 0.000000
[128 rows x 2 columns]
the top of 30 features is Features Importance
0 25 0.467576
1 10 0.055683
2 77 0.031899
3 71 0.030527
4 39 0.025558
5 50 0.022084
6 93 0.019056
7 7 0.018754
8 120 0.015890
9 109 0.015002
10 38 0.014415
11 40 0.012510
12 34 0.011478
13 104 0.010163
14 36 0.010136
15 43 0.009751
16 113 0.009251
17 95 0.009200
18 98 0.007995
19 42 0.007736
20 119 0.007705
21 117 0.006483
22 0 0.006364
23 107 0.005986
the sum of importance is: 0.8312035260103225
- conda create -n env python = 3.7
- conda install scikit-learn
- conda install -c conda-forge rdkit
- pip install seaborn