X = train_encoded_df.iloc[:, 1: ]
y = train_encoded_df["Loan_Status"]
print("Precision:",metrics.precision_score(y_test, y_pred))
My training data contains the categorical features encoded using get_dummies().
This is causing the error:
ValueError: pos_label=1 is not a valid label: array(['N', 'Y'], dtype='U1')
How to fix this?
Topic data-analysis anaconda data python machine-learning
Category Data Science
you can try this :
print("Precision:",metrics.precision_score(y_test, y_pred, average='weighted'))
pos_label is an argument of scikit-learn's precision_score (docs); its purpose is, well, to indicate which label is the positive one and, if not given explicitly (like in your case here), it assumes the default value of 1 (again, check the docs).
Since it seems that the positive label in your case is 'Y', replace the last line with:
print("Precision:",metrics.precision_score(y_test, y_pred, pos_label='Y'))
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