# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications
### Data Analysis and Evaluation
#### 4.2.2 Model Training and Verification  
The partitioning of the dataset into training and testing (prediction) subsets is essential. The training subset is utilized for the \"learning process\" of the ML model, while the testing subset consists of data, which has not been encountered during training. This testing data assesses the ML model's ability to generalize. Although there is no strict guideline regarding the exact percentage distribution between the training and testing sets, it is crucial to ensure that there is an adequate amount of training data for the effective convergence of the ML model [3]. The processes of training and testing are dynamic and ongoing, allowing for additional training and testing as more data becomes available. Table 6 below shows the productivity models' training details. Typically, researchers randomly divide the dataset into three (training, validation, and testing) or two (training and testing) sets during the development of the model. It is apparent that the ratio of the training dataset shall be higher than the testing and/or \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Type of**} & \multirow{2}{*}{**ML Model**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Training Technique**} & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Model**} & \multirow{2}{*}{**Model**} \\  & & & & & & & **Training \%** & & **Validation \%** \\ \hline
[1] & 2012 & Labor & ANN & 90.90\% & Back-propagation & 150 & 60 & 15 & 25 \\
[3] & 2017 & Labor & ANN & 94.43\% & Feed-forward & 29 & 75 & - & 25 \\  & & ANN & & & & & & \\
[4] & 2019 & Labor & (GRNN, BNN, RBFNN, and ANFIS) & 94.9\% & BR & 221 & 80 & - & 20 \\
[5] & 2021 & Labor & SOS-LSVM- & MAPE & LSSVM, SOS, FS, and 10-fold cross & 220 & 90 & - & 10 \\
[6] & 2008 & Labor & ANN & 90\% & Back-propagation & 111 & 80 & - & 20 \\
[15] & 2015 & Labor & ANN + BR & -95\% & Back-propagation & 27 & 70 & 15 & 15 \\
[41] & 2010 & Equipment & BPNN-CGB & Set 3 & Back-propagation & 200 & 85 & - & 15 \\
[43] & 2005 & Equipment & ANN & AVP \textgreater{} & -90\% & Back-propagation & 102 & 70 & 30 & - \\  & & Voiting- & & & & & & & \\
[66] & 2022 & Labor & ensemble and Stacking- & R\({}^{2}\) = 0.7967 & 10-fold cross & 221 & 80 & - & 20 \\  & & ensemble & & & & & & & \\
[86] & 2020 & Labor & ANN-based PIs & At 95\% CL = 92.5\% & Cross, validation, and 221 & 60 & 20 & 20 \\  & & & PICP & simulated annealing & & & & & \\
[87] & 2016 & Labor & NNFR & \(>\)75\% improvement in MSE & ANN, FCM, GA, and 131 & 15 & - & 10 \\
[88] & 2006 & Equipment & MR and ANN & 0.00027 & Back-propagation & N/A & 75 & - & 25 \\
[89] & 2004 & Equipment & CBR & \(\sim\)90\% & N/A & 240 & 90 & - & 10 \\  & & & Act 90\% & & & & & \\
[90] & 2006 & Labor & NN & accuracy & Feed-forward & F = 90 & 90 & 10 \\  & & & F: 91\% & back-propagation & SP = 80 & 50 & - & 50 \\  & & & SP: 97\% & & & & & \\
[91] & 2022 & Labor & DNN; k-NN; SVM; and LR & 97.5\% & Adam optimizer & 3811 & \(\sim\)63 (2400) & \(\sim\)18 (700) & \(\sim\)19 (711) \\
[92] & 2023 & Labor & ANN; ANFIS; and FIS & 93.14\% & Backpropagation; & & & \\  & & & and FIS & (ANFIS & hybrid training; and & 98 & 80 & - & 20 \\  & & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of construction productivity model development details. validation to obtain a more precise accuracy as tested by [PERSON] and [PERSON] [90] with 50:50 and 90:10. In addition, to avoid overfitting problems in the model, the adoption of k-fold cross validation [5; 66; 86] was recommended and commonly used. Furthermore, the further splitting of the training set into three subsets (training, validation, and testing) was performed by [PERSON] and [PERSON] [87] to mitigate the overfitting issues in the model training procedure.