How to use scikit metrics for a statsmodel or vice versa?

Question

Am working on binary classification problem with 5K records. Label 1 is 1554 and Label 0 is 3558.

I did refer this post but not sure whether it is updated now or anyone has any way to compute this metrics

Currently I use logit model as shown below

model = smm.Logit(y_train, X_train_std)
result=model.fit()
y_pred = result.predict(X_test_std)
print("Accuracy is ", accuracy_score(X_test_std, y_pred))  #throws error from here and all the line below
print(classification_report(X_test_std, y_pred))
print("ACU score is ",roc_auc_score(X_test_std, y_pred))
print("Recall score is",recall_score(X_test_std,y_pred))
print("Precision score is",precision_score(X_test_std,y_pred))
print("F1 score is",f1_score(X_test_std,y_pred))

The reason why I am trying to do this is because statsmodel has p-values, coeff, intervals etc and I was hoping to get the usual metrics through scikit metrics as shown above but it isn't accepted.

On the other hand, Through scikit logistic regression I can get usual metrics and coeff, but what about p-values, conf intervals? Is there anyway to do the reverse?

Can someone help me with this?

Answer 1

Interpreting the code (since the error received is not mentioned), it looks like the code is passing in the X matrix and y-pred to the metrics. According to the documentation, the metrics want the y-true and y-pred. This would lead to an error mentioning incorrect dimensions.

I have used statsmodels and call scikit metrics. Many of the scikit examples, like the above documentation, show arrays being passed in, not from a specific model.

If that is not it, please post the error received.