## 4 Machine Learning (ML) Algorithms
### Random Forest (RF)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Pros** & **Cons** \\ \hline Artificial Neural Network (ANN), multi-layer perceptron (MLE), feed-forward (FF), radial basis function (RBF) convolutional (CN) functional, (FN), and probabilistic (PN) & Powerful parallel distributed processing ability, strong distributed storage and learning ability, strong robustness and fault tolerance to noisy nerves, full approximation of complex nonlinear relations, associative memory function, etc. & Numerous parameters are required, including network topology, initial weight and threshold settings; output that is difficult to read; lengthy training period, etc. \\ \hline Particle swarm optimization (PSO) & Free from the problem information, solve problems with real numbers, strong universality, few parameter adjustments, straightforward theory, simple implementation, collaborative search, and rapid convergence. \\ \hline Fuzzy logic & Robustness is strong and straightforward to accomplish; mathematical precision is unnecessary. \\ \hline Support vector machine (SVM) & Appropriate for small-sample machine learning issues; can improve generalization performance, tackle high dimensional problems, address nonlinear problems, and avoid neural network structure selection and the local minimum point problem. \\ \hline Genetic algorithm (GA) & Possibility of parallelism and resilience, simplicity, use of a probability mechanism to perform iterations with a given degree of unpredictability, extensible, and simple to connect with other algorithms. & After determining the ideal solution, complex programming, issue decoding, and more training time are necessary to arrive at an exact solution. \\ \hline Decision tree (DT) & Nonlinearity among parameters has no effect on DT performance; explainable and interpretable. & Complex; duplication may occur for identical subtrees of different routes. \\ \hline K nearest neighbors (KNN) & Classes do not require linear divisibility; modest and strong; understandable and easy to implement technique; can be trained quickly; robust in case of associated noise; it is mainly well suited for multimodal classification. \\ \hline Random forest & High performance gives rise to variable measures; quick in implementation; less complex; robust with noisy data and can learn in increments. & Tends to disregard the attributes’ importance; slow and expensive, and sensitive to local data structure; memory restriction. \\ \hline k-Means & Each data point may exist in several clusters; normalization of gene behavior depiction; a modifiable model that can accommodate varying dataset distributions; if the training data rise, the number of parameters does not alter. & Need to define cluster count c; membership cutoff value must be established; the initial assignment of centroids influences cluster formation; the convergence is slow in some cases. \\ \hline Recurrent neural network (RNN) & Can record the information as timed activations; can manipulate consecutive data where lengths are arbitrary. & It is affected by the vanishing gradient type; it is incompatible with extra-deep modeling stacking. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The pros and cons of commonly used algorithms in the oil and gas field Adapted from [PERSON] et al. (2010).