## 4 Data-Driven Prediction Model
### Palm Oil Prediction Using Supervised ML  
Function learning based on examples of input-output pairs is known as supervised ML. From a labelled set of training examples, it deduces the function from labelled training data, consisting of a set of training examples. Regression analyses, i.e., classification analysis and artificial intelligence, were used to analyse a historical dataset in predicting palm oil  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Author(s)**} & \multirow{2}{*}{**Objective**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Relevant**} & \multicolumn{2}{c}{**Factor Category * \\ \cline{5-8}  & & & & & & **Findings/Performances**} & **E** & **P** & **G** \\ \hline \multirow{4}{*}{[71]} & Breeding & Three hundred twelve tenra & & & & Genomic Estimated & & & \\  & prediction & palms, three & S/F & NJ, chi-square, SNPs & Breeding Value (GEBV) & & & & \\  & prediction & palm oil & & S/F = 0.65\%, 4425 SNP & & & & \\  & breeding & & & & & loci & & \\ \hline \multirow{4}{*}{[82]} & Breeding & Twenty-six & & & & Average polymorphic & & \\  & prediction & leaves of \(E\). & O/B, M/F & UPGMA, SSRs & information content (PIC) & & & \\  & prediction & _guinensis_ palms & & & & & = 0.7325, per contage of & & \\ \hline \multirow{4}{*}{[83]} & \multirow{4}{*}{Breeding} & DxP cross & & & & & & \\  & produced 300 & BW, F/B, & & & & & \\  & during, 25 pisifera, & M/F; K/F, & & & & & & \\  & prediction & 80 tonera, and & O/DM S/F, & & & & & \\  & 100 T\(\times\)T progeny & O/WM, O/B & & & & & \\ \hline \multirow{4}{*}{[84]} & Breeding & Deli Dura and & & NJ, PCA, AMOVA & & & & \\  & prediction & AVROS & F/B & (Analysis of & & & \\  & prediction & & molecular variance) & & & & \\ \hline \multirow{4}{*}{[85]} & Breeding & Eighty-one palm & O/B, & & & & 27 \(E\). _guinensis_ sites were & & \\  & prediction & oil leaves & and & & & & grouped into the dura, & & \\  & & foldichols & & & & & & \\ \hline \multirow{4}{*}{[86]} & Yield & Four hundred & & & & & & \\  & forty-seven & & & & & & & \\  & predictions with a & BUNCH\_HA, & & & & & & \\  & planted area of & ABW & & & & & & \\  & & 19,809 ha & & & & & & \\ \hline \multirow{4}{*}{[87]} & Breeding & Two hundred & & & & NJ & 9\% of SNP loci, 8 189 & & / \\  & prediction & and thirty-six oil & & & Bayesian, SNPs & SNPs & & \\  & & palm leaf tissues & & & & & & \\ \hline \multirow{4}{*}{[88]} & Disease & Thirty-nine oil & & & & & & \\  & prediction & palm trees & Soil moisture & & & & & \\ \hline \multirow{4}{*}{[89]} & FFB & Twenty-seven & & & & & & \\  & ripeness & palm oils Tenera & & & & & & \\  & prediction & & & & & & \\ \hline \multirow{4}{*}{[76]} & Breeding & Five hundred & & & & UPGMA Bayesian & & & \\  & population & and fifty-three & & & & network, DArTseq & & & \\  & prediction & palm oil & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{4}{*}{[90]} & Biomass & Biopower boiler & Environment, & & & & & \\  & palm oil & historical & deaterator, & & & & & \\  & boiler & & & & & & & \\ \hline \multirow{4}{*}{[81]} & Breeding & Two hundred & & & BN, BW, VIR, & & & & \\  & and fifty-one & O/WM, & & & & & & \\  & prediction & Dura cross & O/B, S/F, & AMOVA & & & & \\  & & Pisifera (DxP) & PO, HT, and & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \hline \end{tabular} * Factor Category: E = environment; P = phenotype; G = genotype.  
\end{table}
Table 3: Unsupervised prediction of palm oil (2016–2021).  
yield [36; 38]. The prediction models of palm oil growth were previously studied. Hence, this review will focus on these models and other methods such as Genetic Algorithm (GA), Naive Bayes (NB), Random Forest (RF), and Regression and Support Vector Machine (SVM).