700,000 data points are true values while 1,300,000 data points are false values, is it an imbalanaced dataset? I am trying to build a decision tree model and I have 700,000 true values while 1,300,000 data points are false values, in total, I have 2,000,000 data points including duplicates. I am wondering if the dataset is an imbalanced dataset for a decision tree model.
If this is an imbalanced dataset, can I use 1,000,000 true values and 700,000 false values making them balanced allowing duplicates to build a decision tree model, instead using all of them?
Although I don't know what effect exactly the duplicates will do to the model.

700K and 1.3M is pretty balanced to me, usually people are talking about 1:1000 or even worse as imbalanced data. For example, fraud detection, it can be 1:100000.
In addition, Decision tree (and most machine learning model, such as logistic regression) works fine with the imbalanced data set. Just pay attention to evaluation metrics, and use precision and recall instead of accuracy.
