## 4 Results
### Model testing  
To eliminate the influence of random chance on the test results, Support Vector Machine (SVM), Random Forest (RF), Lightweight Gradient Boosting Machine (LightGBM), and Symmetric Decision Tree Gradient Boosting Machine (CatBoost) were tested 10 times on the test set. The average Balanced Accuracy and F1-score of each model were plotted in bar charts (Figure 15), revealing significant differences in performance across various combinations of data processing methods and models. The combination of UMAP + K-means + CatBoost achieved the best performance in both metrics (Balanced Accuracy = 0.897, F1-Score = 0.872). This indicates that UMAP's nonlinear dimensionality reduction, combined with K-means clustering, effectively enhances CatBoost's ability to capture complex lithological features. The advantage arises from UMAP preserving both global and local data structure, while clustering reflects lithology sub-classes. In contrast, the SVM model performed weakly overall, particularly with PCA and other linear dimensionality reduction methods, highlighting its limited adaptability to high-dimensional and nonlinear data. LightGBM showed strong performance under UMAP and UMAP + K-means due to the efficiency of its histogram algorithm and its ability to capture nonlinear features, though it lagged slightly behind CatBoost, possibly due to less effective gradient bias optimization. Random Forest demonstrated robust performance but with a lower upper \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Parameter** & \
\begin{tabular}{c} **Searching** \\ **Range** \\ \end{tabular} & **Raw** & **PCA** & **UMAP** &
\begin{tabular}{c} **PCA +** \\ **K-Means** \\ \end{tabular} & **k-Means** & **k-Means** \\ \hline SVM & C & 1–50 & 5 & 5 & 5 & 10 & 50 & 50 \\  & \(\gamma\) & 0.01–1.0 & 0.02 & 0.05 & 0.5 & 0.02 & 0.02 & 0.02 \\ RF & min\_samples\_leaf & 2–10 & 2 & 3 & 3 & 3 & 2 & 3 \\  & max\_features & 1–6 & 3 & 3 & 3 & 3 & 3 & 3 \\ LightGBM & min\_samples\_leaf & 2–10 & 3 & 2 & 3 & 2 & 2 & 3 \\  & learning\_rate & 0.02–0.5 & 0.2 & 0.2 & 0.5 & 0.25 & 0.2 & 0.2 \\ CatBoost & depth & 2–10 & 5 & 6 & 6 & 5 & 6 & 6 \\  & learning\_rate & 0.02–0.5 & 0.2 & 0.2 & 0.25 & 0.3 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tuning parameters for SVM, RF, LightGBM, and CatBoost models. limit; while its ensemble learning mechanism reduces noise, it struggles to capture complex interactions as effectively as CatBoost. The improvements from combinations like t-SNE + K-means and PCA + K-means were marginal, sometimes even weaker than dimensionality reduction alone, potentially due to clustering labels being affected by t-SNE's local overfitting or PCA's linear distortion. This comparison underscores UMAP's superior ability to retain geological information and CatBoost's strong adaptability in integrating multi-parameter features. In the study of intelligent lithology classification, the combination of UMAP + K-means and CatBoost demonstrated significant advantages (Figure 16). According to the confusion matrix analysis, CatBoost outperformed other models, particularly in recognizing less common categories like silt and limestone. The recall rate for silt reached 48.6%, significantly higher than SVM's complete failure and LightGBM's 48.6%, with a more balanced distribution of misclassifications. For limestone, the classification accuracy reached 69.0%, which was substantially better than SVM and Random Forest, with a 32% reduction in  
Figure 15: Model test score. misclassification as shale. For more common lithologies, CatBoost's accuracy in identifying fine sand and coarse sand matched that of the ensemble model, but with a lower cross-class misclassification rate. This advantage is attributed to the combined effects of CatBoost's gradient unbiased estimation mechanism and UMAP + K-means. On one hand, CatBoost encodes the cluster labels as interactive features through ordered boosting, capturing the nonlinear relationships between lithology sub-classes and drilling parameters.