## 3 Results
### Classification of Analyzer Overload--Decision Trees and Logistic Regression
The LOWESS method is flexible and can handle nonlinear dependencies between variables. However, the algorithm may be susceptible to the influence of outliers or be unstable in the case of low-density data [18]. The median was used as the most representative indicator. A rolling median was calculated based on historical data from the past 10 runs, followed by another calculation based on the median historical run with a window of 5. A regression coefficient was then computed to allow for slope assessment, assuming a zero intercept (intercept = 0) to maintain the intersection point on the same level for each line in the resulting dataset. The training data were labeled using the'signal' parameter to indicate a binary label for when an alert should be triggered. This signifies the saturation of the analyzer and, specifically, an assessment of saturation risk, requiring the initiation of procedures related to redirecting further investigations to another device. Figure 6: Approximation of models for workload curves for analyzers in different cycles for various analyzers using various statistical characteristics. In the first graph we see several waveforms (daily series) plotted on one scale (blue + orange), on the next two (orange) exemplary daily waveforms are separated. Logistic regression is a popular statistical model used for analyzing categorical data, where the dependent variable takes two possible binary values. Logistic regression is a classification technique that predicts the probability of something belonging to one of two classes based on the values of independent variables, such as characteristics in the form of a rolling median and time. The main idea behind logistic regression is to transform a linear regression model into the logarithm of odds space, which allows for the modeling of the probability of something belonging to a specific class. Unlike linear regression, which predicts continuous values, logistic regression estimates the probability of something belonging to one of two classes. The basic form of logistic regression is a binary model where the dependent variable takes values 0 or 1. This model is based on the logistic function, also known as the sigmoid function, which transforms the results of linear regression into the [0, 1] range [19]. The logistic regression model achieved a satisfactory 96% correct classification rate. Other devices achieved even higher accuracy, with the largest analyzers reaching up to 99.5%. However, some analyzers had an error rate as high as 15.1%, indicating weaknesses in the model that require the careful monitoring of curve variability in the lower range. The error occurred due to temporary, minor intensities in the daily cycle. To address this issue, it is necessary to narrow the variability range in daily cycles, particularly during hours when loading the analyzer to the narrow throat level is not possible. However, calibrating each analyzer model separately is not optimal or feasible when the set of analyzers subjected to such prediction is not closed and constant. To enhance result accuracy, we decided to use a different classifier, specifically a decision tree, along with the slope metric of the curve represented by calculated regression coefficients and a moving median for load profiles. CART (Classification and Regression Trees) models are a widely used method for classification and regression in data analysis. Decision trees are diagrams that show hierarchical decision structures. They allow decisions to be made based on a set of conditions or independent variables. CART constructs a decision tree by dividing the dataset into subsets based on the values of independent variables to minimize heterogeneity (impurity) within each subset [20]. The aim of the optimization was to decrease the maximum load analyzer time by 5% by redirecting a portion of the samples to other devices when maximum efficiency was achieved, with a minimum accuracy of 80%.