This article aims to systematically explore how to apply modern machine learning algorithms to traditional data statistical analysis tasks, in order to solve the bottlenecks faced by traditional methods in high-dimensional, nonlinear, complex interaction, and massive data scenarios. The study first outlined the theoretical framework from classical statistical analysis to machine learning paradigm transfer. At the methodological level, a general analysis framework integrating feature engineering, algorithm selection, and model interpretation is proposed, with a focus on the application logic and applicable conditions of linear models, tree ensemble models (taking random forests and XGBoost as examples), and deep learning models in regression, classification, and clustering tasks. To conduct empirical comparisons, comparative experiments were designed based on three publicly available datasets. The experimental results show that XGBoost achieves the best balance between prediction accuracy and training efficiency on structured data (with an average RMSE reduction of 12.3% and an average accuracy improvement of 4.7%), while random forests exhibit the best stability and feature importance explanatory power. Deep learning models have irreplaceable advantages in image data, but overfitting is prone to occur on small-scale structured data. Finally, this article discusses the bias variance trade-off in algorithm selection, the balance between interpretability and performance, and the future development trend of combining automated machine learning with causal inference. This study provides a systematic decision-making reference for practitioners to select and analyze algorithms based on specific task scenarios.
Dongmei Lin (Thu,) studied this question.