For this I am using phyton and already generated the model has an accuracy of 92%, but use a standard of 70% training and 30% of the dataset. I understand that this is a convention but some other percentage could be used. Taking into account that the data set does not exceed 5000 records.
Dear Guillermo Andrés Arduino ,
Decision Trees are a class of very powerful Machine Learning model cable of achieving high accuracy in many tasks while being highly interpretable. What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure. This structure holds and displays the knowledge in such a way that it can easily be understood, even by non-experts.
https://towardsdatascience.com/a-guide-to-decision-trees-for-machine-learning-and-data-science-fe2607241956?gi=944dc40485ab
Regards,
Shafagat
70-30 is optimal. Further it is better to use cross validation. The number of attributes are also important.
Splitting the dataset into two partitions (one for training and the other one for testing) can result in very variable performance, depending on which observations have ended up in one partition or another. This undesirable effect is even more pronounced when the dataset is particularly limited. To overcome this problem, k-fold cross-validation can be adopted.
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How can someone decide the split ratio for your data 😉. The split depends upon the proportion of the minority numbers in your train and test data.
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