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## Random Forest params
rf_param_grid = {"max_depth": [None],
"max_features": [1, 3],
"min_samples_split": [2, 3],
"min_samples_leaf": [1, 3],
"bootstrap": [False],
"n_estimators" :[100],
"criterion": ["gini"]}
## Extra Trees params
ex_param_grid = {"max_depth": [None],
"max_features": [1, 3],
"min_samples_split": [2, 3],
"min_samples_leaf": [1, 3],
"bootstrap": [False],
"n_estimators" :[100],
"criterion": ["gini"]}
# Gradient Boosting params
gb_param_grid = {'loss' : ["deviance"],
'n_estimators' : [100,200],
'learning_rate': [0.1, 0.05],
'max_depth': [4, 8],
'min_samples_leaf': [100],
'max_features': [0.3]
}
# SVC params
svc_param_grid = {'kernel': ['rbf'],
'gamma': [ 0.001, 0.01, 0.1, 1],
'C': [1, 10, 50, 100,200,300, 1000]}
# AdaBoost params
ada_param_grid = {"base_estimator__criterion" : ["gini", "entropy"],
"base_estimator__splitter" : ["best", "random"],
"algorithm" : ["SAMME","SAMME.R"],
"n_estimators" :[1,2],
"learning_rate": [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3,1.5]}
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