| Keyword | CPC | PCC | Volume | Score | Length of keyword |
|---|---|---|---|---|---|
| information gain formula in decision tree | 1.78 | 0.4 | 4701 | 12 | 41 |
| information | 1.81 | 0.2 | 7552 | 68 | 11 |
| gain | 0.28 | 0.7 | 7450 | 2 | 4 |
| formula | 1.08 | 0.4 | 5869 | 90 | 7 |
| in | 1.03 | 0.1 | 7974 | 48 | 2 |
| decision | 1 | 0.3 | 788 | 39 | 8 |
| tree | 0.73 | 0.8 | 3744 | 28 | 4 |
| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| information gain formula in decision tree | 1.24 | 0.4 | 1280 | 11 |
| information gain in decision tree | 1.94 | 0.6 | 8341 | 30 |
| decision tree using information gain | 0.31 | 0.1 | 364 | 15 |
| build a decision tree using information gain | 0.51 | 0.1 | 5287 | 45 |
| decision tree net gain formula | 1.2 | 1 | 3215 | 70 |
| information gain in decision tree formula | 1.33 | 0.5 | 6100 | 77 |
| information gain in decision tree numerical | 1.83 | 0.2 | 393 | 61 |
| information gain in decision tree python code | 0.46 | 0.7 | 447 | 7 |
| entropy and information gain in decision tree | 0.66 | 0.7 | 6355 | 37 |
| what is information gain in decision trees | 0.02 | 0.7 | 8927 | 69 |
| information gain in decision tree example | 1.55 | 1 | 7506 | 38 |
