# scikit metrics.

If you're going to use optimise a model in scikit-learn then it better optimise towards the right thing. This means that you have to understand metrics in scikit-learn. This series of videos will give an overview in how they work, how you can create your own and how the gridsearch interacts with it.

**Episode Notes**

Here's the code to write your own scorer directly.

```
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_score, recall_score, make_scorer
def min_recall_precision(est, X, y_true, sample_weight=None):
y_pred = est.predict(X)
recall = recall_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
return min(recall, precision)
grid = GridSearchCV(
estimator=LogisticRegression(max_iter=1000),
param_grid={'class_weight': [{0: 1, 1: v} for v in np.linspace(1, 20, 30)]},
scoring={'precision': make_scorer(precision_score),
'recall': make_scorer(recall_score),
'min_both': min_recall_precision},
refit='min_both',
return_train_score=True,
cv=10,
n_jobs=-1
)
grid.fit(X, y);
```

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