## 3 Results
This means that, to achieve reliable quality control required by the Industry 4.0 paradigm, a sufficiently large number of measurements (in the order of several hundred per class) of high quality (after pre-processing) must be provided. Figure 11: Amplitude vs. delamination length (**a**), amplitude vs. crack length (**b**) amplitude vs. rupture length (**c**). Based on the same data set, two solutions were presented as follows: (1) in Table 3: classification results (defect presence or lack of defect as classes respectively defect length = 0, and defect length > 0), (2) in Table 4: regression results (at the time of observation of the position and amplitude, we predict the occurrence of a defect). It is worth noting that this does not exhaust the possibilities of building models based on the proposed data set, also in combination with other sensors or sets of sensors, cameras, etc., within the same production line. Such synergy would probably contribute to improving the quality of products early and more accurate detection of defects but it would be much more expensive, including computationally. During our study, we checked more than one hundred ML algorithms. Tables 3 and 4 show only the best five of them to illustrate the differences in accuracy between them and allow for easier replication of the study using another algorithms and/or other ML environments. Details concerning used in the study ML algorithms from Tables 3 and 4:  
1. FastTreeOva (One-vs-All)
2. Architecture: uses boosted decision trees in a one-vs-all (OvA) scheme, where separate trees are trained for each class;
3. Input parameters: each feature in the input data contributes to the decision process in the trees;
4. Trainable parameters: determined by the number of trees, maximum leaves, and number of classes;
5. Dataset size: works well on medium-sized datasets, it performs best with less noisy data and a balance between classes;
6. Hyperparameters: \(\bigcirc\) NumberOfLeaves: controls the number of terminal nodes in each tree (default ~20); \(\bigcirc\) NumberOfTrees: total number of boosting rounds or trees (default ~100); \(\bigcirc\) LearningRate: adjusts the weight update (default ~0.2); \(\bigcirc\) MinimumExampleCountPerLeaf: minimum instances required to form a leaf (default ~10); \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **RSquared** & \begin{tabular}{c} **Absolute-** \\ **Loss** \\ \end{tabular} & \
\begin{tabular}{c} **Squared-** \\ **Loss** \\ \end{tabular} &
\begin{tabular}{c} **RMS-Loss** \\ \end{tabular} \\ \hline FastForestRegression & 0.9721 & 0.00 & 0.00 & 0.50 \\ \hline SdcaRegression & 0.9211 & 0.00 & 0.00 & 0.40 \\ \hline LbfgsPoissonRegressionRegression & 0.8915 & 4.74 & 4.12 & 5.32 \\ \hline FastTreeTreeedieRegression & 0.8712 & 4.92 & 4.33 & 5.51 \\ \hline FastTreeRegression & 0.8622 & 4.97 & 4.79 & 5.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression results (at the time of observation of the position and amplitude, we predict the occurrence of a defect) for the aggregated dataset—five best algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Microaccuracy** & **Macroaccuracy** \\ \hline FastTreeOva & 0.9518 & 0.9501 \\ \hline SdcaLogisticRegressionOva & 0.9431 & 0.9412 \\ \hline SdcaMaximumEntropyMulti & 0.8991 & 0.8711 \\ \hline LightGbmMulti & 0.8812 & 0.8701 \\ \hline LbfgsLogisticRegressionOva & 0.8712 & 0.8703 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results (defect presence or lack of defect as classes respectively defect length = 0, and defect length > 0) for the aggregated dataset—five best algorithms. 2.