How can I explore a Custom ML model runned on Dataiku succesfully but with no description at the end ?
I have tried to deploy the model and import it in a python recipe to understand the configuration (best fit model, parameters,...) but I have a Dataiku object in the recipe, and I don't know the functions for dataiku object.
Here is the Gridsearch/ML model:
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import GridSearchCV
# Pipeline definition with preprocessing ( Robustscaler)
randomF = {}
randomF['pipeline'] = Pipeline([
('scaler', RobustScaler()),
('rf', RandomForestClassifier())
])
randomF['hyperparameters'] = {}
randomF['hyperparameters']['rf__n_estimators'] = [50, 100, 150, 200]
randomF['hyperparameters']['rf__criterion'] = ['gini', 'entropy']
randomF['hyperparameters']['rf__min_samples_split'] = [2 , 5]
randomF['hyperparameters']['rf__max_depth'] = [20, 5, 10]
randomF['hyperparameters']['rf__min_samples_leaf'] = [2,3]
randomF['hyperparameters']['rf__bootstrap'] = [True, False]
randomF['hyperparameters']['rf__class_weight'] = [None, 'balanced', 'balanced_subsample']
randomF['gridsearch'] = GridSearchCV(randomF['pipeline'],
randomF['hyperparameters'],
scoring = "neg_log_loss",
cv = 5,
n_jobs = -1,
refit = True,
fit_params=None,
iid=True,
verbose=0,
pre_dispatch='2*n_jobs'
)
clf = randomF['gridsearch']
