I am working on classification of time series multivariate data. By doing PCA, I converted multivariate to uni-variate and fed it into a conv1d in keras.
However, I am getting a very high accuracy and low loss both in validation and in training. How can I justify this?
I have tried cross validation, but the results are not much different. I am using adam optimizer (learning rate:0.0001). With 0.001, my model fails to converge.
I have made sure that I am not mixing the training and validation datasets. I have shuffle both datasets independent of each other. I trained on 3728 samples and validated on 610 samples.
Can we expect such a high accuracy with binary classification?