It is well known that a Cross-validation is an important technique often used in machine learning to assess both the variability of a dataset and the reliability of any model trained using that data.
I want some input on use cases cross-validation model in agriculture.
Hi,
You need to train your classifier on the data that are obtained from such domain, first. Cross-validation is going to be used for evaluation proposes (i.e., evaluate the resulting predictive model)--nothing special with it.
HTH.
Dr. Samer Sarsam
Dear Ajit
The data sets in Animal genetics and breeding has traditionally been bigger than in many of its contemporary biological sciences, and developing efficient algorithms have been an important and relevant activity in this field.
to do cross validation for evaluation is not a big deal when datasets are huge as the chances of overfitting is very low. you can just use Leave-p-out cross-validation methods for animal husbandry data
There is more to cross-validation than what has been mentioned here. You need to perform cross-validation on hyperspace grid search (or its variants) to find out the near-optimum model hyperparameters to tune your model. Think of this as an effective means to address the bias-variance tradeoff without looking at the test data. My recommendation is to always use cross-validation regardless of the dataset size and the field of study (i.e., Agriculture is included).
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