I’m not sure if I could use boxcox transofmation for normalization. If I can, here is how I’m writing for the transformation part:
from scipy.stats import boxcox
def boxcoxtrans(df):
df = df.copy(deep=True)
trans, lambda_value = boxcox(df[“FTSE_Small”])
df[“FTSE_Small_boxcox”] = trans
print(trans)
return df
When I apply this function to X_train:
X_train = boxcoxtrans(X_train)
I will get the lambda_value associated with this transformation.
Do I use this lambda_value to transform X_test? Also, later when I’m using the transformed data for Deep Learning Bianry Classification, do I need to tranform the data back to the original scale? Or simply passing the transformed data to deep learning is fine?
I am a little confused. Hopefully I have expressed myself clear enough.