def make_adaboost_tree_params():
params = process_data.make_predictor_params()
adaboost_params = {
"model__n_estimators": [20, 40, 60, 80, 100, 120],
"model__learning_rate": [0.25, 0.5, 0.75, 0.8, 1.0],
}
params.update(adaboost_params)
return params
def make_svm_params():
params = process_data.make_predictor_params()
svm_params = {
"model__kernel": [ "linear", "poly", "rbf", "sigmoid" ],
"model__degree": [ 1, 2, 3, 4, 5 ],
"model__gamma": [ "auto", "scale" ],
"model__coef0": [ -0.1, 0.0, 0.1 ],
}
params.update( svm_params )
return params
def make_svm_params():
params = process_data.make_predictor_params()
svm_params = {
'model__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'model__degree': [1, 2, 3, 4, 5],
'model__gamma': ['auto', 'scale'],
'model__coef0': [-0.1, 0.0, 0.1]
}
params.update(svm_params)
return params
def make_bagging_tree_params():
params = process_data.make_predictor_params()
bagging_params = {
"model__n_estimators": [10, 20, 40, 80, 100, 120, 140],
"model__max_samples": [0.25, 0.4, 0.5, 0.6, 0.75, 1.0],
"model__max_features": [0.25, 0.4, 0.5, 0.6, 0.75, 1.0],
"model__bootstrap": [True, False],
"model__bootstrap_features": [True, False],
}
params.update(bagging_params)
return params
def make_decision_tree_params():
params = process_data.make_predictor_params()
tree_params = {
'model__max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None],
'model__min_samples_split':
[0.001, 0.002, 0.003, 0.004, 0.005,
0.006], #0.007, 0.008, 0.009, 0.01, 0.05, 0.10, 0.20
'model__criterion': ['gini', 'entropy'],
'model__splitter': ['best', 'random'],
'model__min_samples_leaf':
[0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007,
0.008], #0.009, 0.01, 0.05, 0.10, 0.20
'model__max_leaf_nodes': [2, 4, 8, 16, None],
'model__min_impurity_decrease':
[0.000, 0.001, 0.002, 0.005, 0.010, 0.10]
}
params.update(tree_params)
return params
def make_decision_tree_params():
params = process_data.make_predictor_params()
tree_params = {
"model__max_depth": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, None],
"model__min_samples_split": [
0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,
0.01, 0.05, 0.10, 0.20
],
"model__criterion": ["gini", "entropy"],
"model__splitter": ["best", "random"],
"model__min_samples_leaf": [
0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,
0.01, 0.05, 0.10, 0.20
],
"model__max_leaf_nodes": [2, 4, 8, 16, None],
"model__min_impurity_decrease":
[0.000, 0.001, 0.002, 0.005, 0.010, 0.10],
}
params.update(tree_params)
return params
