fixed the issue. Will look into that further I am learning to use scikit-learn as an alternative to R/SAS EM to produce machine learning models. I can produce a logistic regression classifier and apply it to a test set but I cannot seem to determine how to view the regression formula? I understand that I cannot save out as a PMML and only use joblib or pickle dumps, but these are not very intuitive. , After training classifier

code :

```
from sklearn.linear_model import LogisticRegression
# generating some dataset
from hep_ml.commonutils import generate_sample
X, y = generate_sample(n_samples=1000, n_features=10)
trained_regressor = LogisticRegression().fit(X, y)
```

```
trained_regressor.coef_
```

```
array([[ 0.85468364, 1.09829236, 1.19397439, 0.89664885, 0.81402396,
1.00528498, 1.11475434, 0.88583092, 0.708134 , 0.76573151]])
```

```
scores = safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_
```