How to compare expected and predicted values?

0 votes
from sklearn.datasets import load_boston
boston_data = load_boston()
df = pd.DataFrame(boston_data.data , columns = boston_data.feature_names)
df
x = df
y = boston_data.target
x.columns
reg.fit(x,y)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y)
reg.fit(x_train, y_train)
predicted = reg.predict(x_test)
expected = y_test

How to I compare the predicted and expected values to understand the model?

Jul 13, 2019 in Python by Rishi
196 views

1 answer to this question.

0 votes

The predict() function returns a plain numpy array you can just represent it in a tabular format with original value to see the difference.

To check the accuracy of your model you can check out the RMS value. You can calculate RMS using the below code. 

import numpy as np
print("RMS: %r " % np.sqrt(np.mean((predicted - expected) ** 2)))
answered Jul 13, 2019 by Tina

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