Memenangi Kompetisi Kaggle dengan Python
Yauhen Babakhin
Kaggle Grandmaster
| Model | RMSE validasi | RMSE LB publik | Peringkat LB publik |
|---|---|---|---|
| Rata-rata sederhana | 9,986 | 9,409 | 1449 / 1500 |
| Rata-rata per grup | 9,978 | 9,407 | 1411 / 1500 |
| Gradient Boosting | 5,996 | 4,595 | 1109 / 1500 |
| Tambah fitur jam | 5,553 | 4,352 | 1068 / 1500 |
| Tambah fitur jarak | 5,268 | 4,103 | 1006 / 1500 |
| ... | ... | ... | ... |
| Model | RMSE validasi | RMSE LB publik | Peringkat LB publik |
|---|---|---|---|
| Rata-rata sederhana | 9,986 | 9,409 | 1449 / 1500 |
| Rata-rata per grup | 9,978 | ||
| Gradient Boosting | 5,996 | 4,595 | 1109 / 1500 |
| Tambah fitur jam | 5,553 | ||
| Tambah fitur jarak | 5,268 | 4,103 | 1006 / 1500 |
| ... | ... | ... | ... |
| Jenis kompetisi | Rekayasa fitur | Optimasi hyperparameter |
|---|---|---|
| Machine Learning klasik | +++ | + |
| Deep Learning | - | +++ |
$$Loss = \sum_{i=1}^{N}{(y_i - \hat{y}_i)^2} \to \min$$
$$Loss = \sum_{i=1}^{N}{(y_i - \hat{y}_i)^2} \to \min$$
$$Loss = \sum_{i=1}^{N}{(y_i - \hat{y}_i)^2 + \alpha\sum_{j=1}^{K}{{w_j}^2}} \to \min$$


# Possible alpha values alpha_grid = [0.01, 0.1, 1, 10]from sklearn.linear_model import Ridge results = {} # For each value in the grid for candidate_alpha in alpha_grid:# Create a model with a specific alpha value ridge_regression = Ridge(alpha=candidate_alpha)# Find the validation score for this model# Save the results for each alpha value results[candidate_alpha] = validation_score
Memenangi Kompetisi Kaggle dengan Python