## 2 Development of a Servo Valve Prediction Model
### Feature Selection  
A common problem in machine learning is identifying a set of relevant features to build a classification model. The most crucial feature selection task is eliminating inappropriate and redundant data, which helps improve the performance of learning algorithms. Feature selection (FS) is crucial in machine learning; it is the primary method for collecting and categorizing data and it occupies an extremely important place in research areas such as computer pattern recognition. Four main benefits of FS are the elimination of inappropriate and unnecessary features, making data mining tasks easier, improving accuracy, and simplifying and making conditional models easier to understand.  
The simple variable importance metric used in the tree-based integration approach is to count only the number of times each variable is selected by all individual trees in the integration. A more refined variable importance metric consists of the improved (weighted) average of the splitting criteria generated by individual trees for each variable. An example of such a classification metric is the \"Gini importance\" available in random forest implementations. \"Gini importance\" describes an improvement in the \"Gini gain\" splitting criterion.  
The most advanced variable importance measure available in random forests is the \"permutation accuracy importance\" measure. The basic principle is as follows: by randomly ranking the predictor variable \(X_{j}\), its original association with the response \(Y\) is broken. When the \(X_{j}\) replacement variable is used with the remaining un-replaced predictor variables to predict the response, the prediction accuracy (i.e., the number of correctly classified observations) is significantly reduced if the original variable \(X_{j}\) is associated with the answer. Thus, a reasonable measure of variable importance is the difference in prediction accuracy before and after the replacement of \(X_{j}\).  
For variable selection purposes, the advantage of the random forest ranking accuracy importance measure over univariate screening methods is that it covers the effects of each predictor variable individually as well as multivariate interactions with other predictor variables.  
Feature selection is selecting relevant features in a dataset based on a subset of features obtained from the initial set of features according to specific feature selection criteria. The main function of this process is to compress the data and analyze them accordingly, removing certain features from the data that have little relevance and semantic overlap. Feature selection techniques can pre-process learning algorithms, and good feature selection can achieve more accurate purposeful learning, improve learning efficiency, and maximize learning outcomes. Therefore, the dimensionality reduction method can be performed by first completing the feature selection link and then extracting the obtained features. Unlike feature selection, feature extraction usually requires transforming the original data into parts with strong pattern recognition capability. The original data can be regarded as features with weak recognition capability.