How can I approach a numeric prediction task to predict the "chol" attribute using the dataset located at $/$KDrive$/$SEH$/$SCSIT$/$Students$/$Courses$/$COSC$2111/$DataMining$/$data$/$arff$/$numeric$/$cholesterol$.$arff? Specifically, how do I:

Run classifiers such as ZeroR, M$5$P$,$ and IBk with default parameters and compare their training and cross$-$validation errors? What conclusions can I draw from the results?

Experiment with different parameter settings for the M$5$P and IBk classifiers to find the optimal settings that provide the best predictive accuracy while minimizing overfitting? How should I select and interpret the best five runs for these classifiers?

Identify and test two additional classifiers for numeric prediction, analyze their parameters, and select the best five runs based on predictive accuracy and overfitting. Which classifier demonstrates the best overall performance?

Discover any valuable insights $("$golden nuggets"$)$ during this analysis?

Could you provide guidance on these tasks, including strategies for testing, interpreting results, and reporting findings?