## 2 Development of a Servo Valve Prediction Model
### Support Vector Machines (SVM)
Machine learning algorithms always have some parameters and settings on which the efficiency of these methods depends. Decision tree learning algorithms have their own set of settings and parameters, which determine the nature of the algorithm. This means that we get a different algorithm to learn the decision tree by changing these parameters. Over the past two decades, random forest (RF) classifiers have received increasing attention due to the excellent classification results and processing speed achieved. RF classifier uses predictions from a collection of decision trees to produce reliable classifications. Furthermore, the classifier can successfully select and rank the variables with the greatest ability to distinguish between target categories. This is an essential asset as the high dimensionality of remotely sensed data makes the selection of the most relevant variables a time-consuming, error-prone, and subjective task. Many studies have systematically investigated the use of RF classifiers for hyperspectral data classification and enhanced thematic map (ETM+) or multispectral scanner (MSS) land cover (LC) classification and digital elevation model (DEM) data. However, publications have yet to specifically summarize the use of such versatile and efficient classifiers in different application scenarios. A random forest (RF) classifier is an integrated classifier of a decision tree, which is generated from a randomly selected subset of training samples and variables. This classifier has become popular in remote sensing due to its classification accuracy. The overall objective of this work is to review the use of RF classifiers in remote sensing, which are superior in dealing with high-dimensional data and multicollinearity problems, avoiding the sensitive problem of overfitting and enabling fast classification. However, it is susceptible to sampling design. The variable importance (VI) measure provided by the RF classifier has been widely used in different scenarios, such as reducing the dimensionality of hyperspectral data, identifying the most relevant multi-source remote sensing and geographic data, and selecting the most appropriate season to classify a specific target class. Less common uses of the classifier need to be further investigated, such as for sample proximity analysis to detect and remove outliers from training samples. Xgboost  
Xgboost is an acronym for the eXtreme Gradient Boosting package. It is an efficient and scalable implementation of the gradient boosting framework. The package includes efficient linear model solvers and tree-learning algorithms. It supports the learning of a wide range of objective functions. The box is scalable and allows the program developer to determine the objective function independently. Xgboost is a supervised learning algorithm that implements a process called boosting to produce accurate models. Supervised learning is inferring a predictive model from a set of labelled training examples. This predictive model can then be applied to new unseen examples. Supervised learning can be used to solve classification or regression problems.