Fig. 1

Classification algorithm. Logistic regression was used to calculate the probability of a binary ending event, fitting an S-shaped probability curve. Support vector machine is a binary classification model based on the principle of finding the hyperplane in a three-dimensional scatter plot that divides the dataset into two categories for smaller datasets. Neural networks are complex interconnected regression layers, such as biological neural networks in the human brain. Neural networks benefit from a large amount of data. Decision trees (e.g. random forests and gradient boosting machines, where random forests are algorithms that integrate multiple decision trees; gradient boosters are algorithms that iterate through multiple decision trees to improve predictive power) use a flowchart-like structure for decision-making that is easy to understand and visualize. The data points are split into similar categories (each "branch in the tree", so-called splitting points) at a given time. Regularized discriminant analysis can reduce the dimensionality of a binary ending dataset with many features and avoid overfitting to achieve sample balancing