How to print Recall and Accuracy along with Parameters used in a GridSearch in Sklearn? - pandas

Question

I want to print the accuracy,recall along with each parameters used in Grid, How that can be done.
My Gridsearch code
from sklearn.grid_search import GridSearchCV
rf1=RandomForestClassifier(n_jobs=-1, max_features='sqrt')
#fit_rf1=rf.fit(X_train_res,y_train_res)
# Use a grid over parameters of interest
param_grid = {
"n_estimators" : [50, 100, 150, 200],
"max_depth" : [2, 5, 10],
"min_samples_leaf" : [10,20,30]}
from sklearn.metrics import make_scorer
from sklearn.metrics import precision_score,recall_score
scoring = {'precision': make_scorer(precision_score), 'Recall': make_scorer(recall_score)}
CV_rfc = GridSearchCV(estimator=rf1, param_grid=param_grid, cv= 10,scoring=scoring)
CV_rfc.fit(X_train_res, y_train_res)
My Expected Output
{'max_depth': 10, 'min_samples_leaf': 2, 'n_estimators': 50,'accuracy':.97,'recall':.89}
{'max_depth': 5, 'min_samples_leaf':10 , 'n_estimators': 100,'accuracy':.98,'recall':.92}

Answer 1

If you set scoring as a list of scorers, you can get the mean score for each scorer in CV_rfc.cv_results_.
For example:
from sklearn.datasets import make_classification
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
X, y = make_classification()
base_clf = RandomForestClassifier()
param_grid = {
"n_estimators" : [50, 100, 150, 200],}
CV_rf = GridSearchCV(base_clf, param_grid, scoring=['accuracy', 'roc_auc'], refit=False)
CV_rf.fit(X, y)
print(CV_rf.cv_results_)
and you get output like:
{'mean_fit_time': array([ 0.05867839, 0.10268728, 0.15536443, 0.19937317]),
'mean_score_time': array([ 0.00600123, 0.01033529, 0.0146695 , 0.02000403]),
'mean_test_accuracy': array([ 0.9 , 0.91, 0.89, 0.91]),
'mean_test_roc_auc': array([ 0.91889706, 0.94610294, 0.94253676, 0.94308824]),
'mean_train_accuracy': array([ 1., 1., 1., 1.]),
'mean_train_roc_auc': array([ 1., 1., 1., 1.]),
[...]
}
So the mean_test_[scoring] is what you are after. Note that you can import cv_results_ as a Pandas DataFrame. That helps readability a lot!