Optimizations of Modified Machine Learning Algorithms Using K-Fold Cross Validations for Wheat Productivity: A Hyper Parametric Approach
Optimizations of Modified Machine Learning Algorithms Using K-Fold Cross Validations for Wheat Productivity: A Hyper Parametric Approach
Farrukh Shehzad1, Muhammad Islam1,2*, Muhammad Omar3, Syed Ijaz Hussain Shah4, Rizwan Ahmed5 and Naeem Sohail6
Figure 1:
Machine learning and traditional programming paradigms.
Figure 3:
The K-Fold cross validation hyper parametric tuning.
Figure 5:
Heat plot map of correlation matrix for D2.
Figure 4:
Heat plot map of correlation matrix for D1.
Figure 6:
Heat plot map of correlation matrix for D3.
Figure 7:
Heat plot map of correlation matrix for D4.
Figure 8:
K-Fold cross-validations for the modified machine learning models.
Equation 1
Equation 2
Equation 3
Figure 2:
Comprehensive flowchart for supervised machine learning algorithms.
November 2022
Vol.38, Iss. 5, Pages 1-396
