# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 2 Machine Learning Models  
A tree model has many analogies in real life, and it has influenced a wide area of machine learning. The well-known tree-based models include decision trees (DT), random forest (RF), gradient boosted trees (GBT), and two kinds of hybrid trees (DT-GBT and RF-GBT).  
**Decision Trees (DT)** are hierarchical structures with nodes that are used to estimate numerical target values or forecast class affiliations. DT models start at the root node and apply the splitting criteria based on distinct attributes at each node to forecast the class label for a record. Different attributes are analyzed at each internal node, and the one that offers the most significant information gain is chosen [35]. It is best to choose the ideal tree size adaptively. Various maximum depths can be attempted during the model optimization process to discover the best tree size for balance accuracy and model simplicity. As a result, the model's forecast abilities are enhanced because over-fitting and under-fitting are prevented.  
**Random Forest (RF)** creates a \"forest\" by using a bagging technique to combine several decision trees. Using bootstrap sampling, the RF model trains each tree using a random fraction of the initial training data. Since RF offers averaged estimations across several aggregations, it fundamentally differs from individual decision trees [36]. Two user-defined parameters, forest size, and predictor subsets, are essential for creating an RF model [37]. The RF model can overcome the disadvantage of a single decision tree in over-fitting the training data and can handle data with some missing values.  
**Gradient Boosted Trees (GBT)** comprise an ensemble machine learning model that uses a boosting technique to combine a number of decision trees. The learning process in GBT follows a step-by-step progression and finally achieves a strong learner from sequentially connected weak learners (i.e., individual decision trees) [38]. Each added tree deals with the error from the previous set of trees, boosting a highly efficient and accurate forecast. The idea behind GBT is very different from that of RF. The RF builds all decision trees in parallel, and the output of RF is the average of prediction results from all decision trees, whereas GBT builds decision trees in the form of a sequence, and the output of GBT is the sum of forecast results from all decision trees [39].  
**Hybrid DT-GBT** combines forecasts from two learners (DT and GBT) by using the vote operator and the nested subprocess. The vote operator comprises mainstream aggregation techniques to combine various forecasts to produce the best forecast. Both classification and regression scenarios can benefit from such a hybrid, which takes advantage of both GBT and DT. In classification, the class of an ambiguous instance is forecasted using a majority vote, while in regression, it calculates the average of forecasts produced from two learners to forecast a continuous value in regression problems.  
**Hybrid RF-GBT** is a hybrid of RF and GBT. The advantages of both techniques are fully utilized in the hybrid method to improve forecast accuracy and stability. Stacking, in which forecasts from both models are used as features in a meta-model, and averaging, in which forecasts are merged to produce the output, are two mainstream operators in the hybrid RF-GBT model. This model has shown enhanced performance in comparison to using two models separately, making it a useful tool for managing various data patterns, capturing intricate linkages, and obtaining greater generalization on unobserved data.