1. Overfitting: This is the major problem associated with the Decision Trees. It generally leads to overfitting of the data which ultimately leads to wrong predictions for testing data points. it keeps generating new nodes in order to fit the data including even noisy data and ultimately the Tree becomes too complex to interpret. In this way, it loses its generalization capabilities. Therefore, it performs well on the training dataset but starts making a lot of mistakes on the test dataset.
2. High variance: As mentioned, a Decision Tree generally leads to the overfitting of data. Due to the overfitting, there is more likely a chance of high variance in the output which leads to many errors in the final predictions and shows high inaccuracy in the results. So, in order to achieve zero bias (overfitting), it leads to high variance due to the bias-variance tradeoff.
3. Unstable: When we add new data points it can lead to regeneration of the overall Tree. Therefore, all nodes need to be recalculated and reconstructed.
4. Not suitable for large datasets: If the data size is large, then one single Tree may grow complex and lead to overfitting. So in this case, we should use Random Forest instead, an ensemble technique of a single Decision Tree.