I think it would be valuable at some point to explore what parameters are most important to optimize for the various models used in TPOT, as I discussed here.
_DEFAULT_PARAM_GRIDS = {'AdaBoostClassifier':
[{'learning_rate': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'AdaBoostRegressor':
[{'learning_rate': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'DecisionTreeClassifier':
[{'max_features': ["auto", None]}],
'DecisionTreeRegressor':
[{'max_features': ["auto", None]}],
'ElasticNet':
[{'alpha': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'GradientBoostingClassifier':
[{'max_depth': [1, 3, 5]}],
'GradientBoostingRegressor':
[{'max_depth': [1, 3, 5]}],
'KNeighborsClassifier':
[{'n_neighbors': [1, 5, 10, 100],
'weights': ['uniform', 'distance']}],
'KNeighborsRegressor':
[{'n_neighbors': [1, 5, 10, 100],
'weights': ['uniform', 'distance']}],
'Lasso':
[{'alpha': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'LinearRegression':
[{}],
'LinearSVC':
[{'C': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'LogisticRegression':
[{'C': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'SVC': [{'C': [0.01, 0.1, 1.0, 10.0, 100.0],
'gamma': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'MultinomialNB':
[{'alpha': [0.1, 0.25, 0.5, 0.75, 1.0]}],
'RandomForestClassifier':
[{'max_depth': [1, 5, 10, None]}],
'RandomForestRegressor':
[{'max_depth': [1, 5, 10, None]}],
'Ridge':
[{'alpha': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'SGDClassifier':
[{'alpha': [0.000001, 0.00001, 0.0001, 0.001, 0.01],
'penalty': ['l1', 'l2', 'elasticnet']}],
'SGDRegressor':
[{'alpha': [0.000001, 0.00001, 0.0001, 0.001, 0.01],
'penalty': ['l1', 'l2', 'elasticnet']}],
'LinearSVR':
[{'C': [0.01, 0.1, 1.0, 10.0, 100.0]}],
'SVR':
[{'C': [0.01, 0.1, 1.0, 10.0, 100.0],
'gamma': [0.01, 0.1, 1.0, 10.0, 100.0]}]}