*3.1. Machine Learning Algorithms*

## 3.1.1. Linear Regression

Linear regression (LR) can be classified as univariate linear regression, and multivariate linear regression, which establishes a functional relationship between the dependent and independent variables and is a supervised machine learning algorithm [81]. This paper uses multivariate linear analysis because of the large number of independent variables. Its expression is given in Equation (4).

$$\mathcal{Y} = \beta 0 + \sum\_{j=1}^{m} Xj\beta j \tag{4}$$

where *Y* denotes the dependent variable, which in this paper represents the shear force at the end of the beam when PE debonding occurs and the strain of the sheet at the middle of the beam when IC debonding occurs; *β<sup>0</sup>* is the regression constant; *m* denotes the number of independent variables; *Xj* denotes the independent variable, which in this paper is the parameter for predicting the debonding; and *β<sup>j</sup>* is the regression coefficient. The root mean square error is minimized by the gradient descent method, and the best combination of regression coefficients is subsequently obtained, which in turn leads to the best-fit line.

## 3.1.2. Ridge Regression

The ridge regression (RR) is a modified linear regression method. By giving up the unbiased nature of linear regression, the regression method obtains more realistic and reliable regression coefficients at the cost of losing some information and accuracy, and the fit to the pathological data is stronger than that of the ordinary linear regression method [82].

#### 3.1.3. Decision Tree

The decision tree (DT) is a classic machine learning algorithm. It mainly consists of nodes and directed edges. There are two types of nodes: the internal node and the leaf node. Internal nodes represent features or attributes, and leaf nodes represent classes or values. When the regression is performed with a decision tree, each feature of the sample is tested from the root node, and the sample is assigned to its child nodes according to the test results; at this time, each child node corresponds to one of the values taken for the feature. The samples are tested and assigned in this way recursively until they reach the leaf nodes [83].
