Comments (3)
You are basically saying the out of sample/in-sample prints were flipped? I made that adjustment and also got rid of the stray .format(score1).
from t81_558_deep_learning.
I recall they being two typos, I saw only one now
df_normal = df[normal_mask]
x_normal = df_normal.values
x_normal_train, x_normal_test = train_test_split(x_normal, test_size=0.25, random_state=42)
pred = model.predict(x_normal)
score2 = np.sqrt(metrics.mean_squared_error(pred,x_normal))
print(f"Insample Normal Score (RMSE): {score2}")
Regarding the last line, to my understanding, Insample implies it being from the training set. However, this appears to be all (normal) date
Sorry if I'm mistaken. Please feel free to close the request when ever you wish
from t81_558_deep_learning.
Added more description. Training occurred entirely on normal data so the insample and out of sample both come from just normals. The final RMSE reports the error on the non-normal, which is higher, indicating an anomoly.
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Related Issues (20)
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