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I am using the random forest.My test accuracy is 70% on the other hand train accuracy is 34% ? what to do ? How can I solve this problem.
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Improve Your Model's Evaluation Accuracy If your model's accuracy on your testing data is lower than your training or validation accuracy, it usually indicates that there are meaningful differences between the kind of data you trained the model on and the testing data you're providing for evaluation.
Training accuracy means that identical images are used both for training and testing, while test accuracy represents that the trained model identifies independent images that were not used in training.
Theoretically, it is possible to have a higher test accuracy than the validation accuracy.
Test accuracy should not be higher than train since the model is optimized for the latter. Ways in which this behavior might happen:
you did not use the same source dataset for test. You should do a proper train/test split in which both of them have the same underlying distribution. Most likely you provided a completely different (and more agreeable) dataset for test
an unreasonably high degree of regularization was applied. Even so there would need to be some element of "test data distribution is not the same as that of train" for the observed behavior to occur.
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