## 4 Data-Driven Prediction Model
### Palm Oil Prediction Using Supervised ML
#### 4.2.1 Types of Regression  
Learning can be achieved using data and regression algorithms in an ML environment to minimise the observed loss or error. Regression is a supervised learning model to predict an output variable based on the known data variables. The objective of the regression process is to predict a continuous quantity from a set of input variables. In other words, a function that accounts for the observed quantity is approximated using relevant variables. Linear Regression (LR) and Multiple Linear Regression (MLR) are standard palm oil yield prediction algorithms. Accordingly, various complex regression algorithms were developed, such as support vector regression (SVR), pairwise regression (PR), principal component regression (PCR), and others.  
A commonality among regression-based methods exists because the goal entails the determination of the optimum fit between the two variables. However, the most significant differences between the two methods are the type of image and the input variables used. The LR model depicts the link between one or more independent and dependent variable [91]. Meanwhile, multi-independent variables are more reliable than single ones using the MLR model [92; 93], commonly conducted using the least-squares method (LSM).  
Furthermore, the generalised cross-validation (GCV) index, which uses the MLR method to account for nonlinearity, can be used to analyse the MARS model [94; 95]. A study proposed LR to predict the vegetative components of oil palms [96]. Similarly, [PERSON] [97] suggested a prediction method for LR, SVR, and MLP combined with a time series model and intelligent nonlinear model. Notably, this approach improves the error caused by time series analysis. Table 4 comprehensively illustrates regression predictions derived from previous studies.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors** & **Objective** & **Dataset** & **Feature** & **Methods** & **Review Findings/References** & \multicolumn{2}{c}{**Factor Category \({}^{*}\)**} \\ \cline{6-10}  & & & & & & & **E** & **P** & **G** \\ \hline
[9] & Crop yield prediction & Historical data & & WOOSST crop model outputs, ensemble, crop area, ensemble, model size, cycle, field size & \multicolumn{2}{c}{3 NN regression} & \multicolumn{2}{c}{\(p\)value of 0.9} & / / \\ \cline{6-10}  & & & & & & **SWR** & & \\ \hline
[9] & Lead in palm often often of prediction & Fourier-transform learned (STIR) data & Lent, pump palm often oil, and the additional cells in 20,25 and 50% different sequences & SL & RMSE = 174.86 & / \\ \cline{6-10}  & & & & & & **RMSE** & 1-14.84 & / \\ \cline{6-10}  & & & & & & & \\ \hline
[9] & Deferentiative population & Tree plantation in 2014 & Environment and historical & LR & Accuracy = 98.25\% & / \\ \hline
[100] & Meta plan prediction & Inventory-flow palm cells & Humidity, fixed number & LR & \(K^{\prime}=0.01\) & / \\ \cline{6-10}  & & & & & **FR** & \(K^{\prime}=0.01\) & / \\ \cline{6-10}  & & & & & **ANN** & \(K^{\prime}=0.04\) & \\ \hline
[101] & Leaf nutrient content prediction & Leaf reflectance data & K, N, and Mg & \multicolumn{2}{c}{FSR (Partial heart-squares)} & \multicol