Random Forest hyperparameter tuning scikit-learn using GridSearchCV

Question

I am trying to use Random forest for my problem (below is a sample code for boston datasets, not for my data). I am planning to use GridSearchCV for hyperparameter tuning but what should be the range of values for different parameters? How will I know that the range I am selecting is the correct one?

I was reading about it on the internet and someone suggested to try "zoom in" on the optimum in a second grid-search (e.g. if it was 10 then try [5, 20, 50]).

Is this the right approach? Shall I use this approach for ALL the parameters required for random forest? This approach may miss a "good" combination, right?

import numpy as np
from sklearn.grid_search import GridSearchCV
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestRegressor
digits = load_boston()
X, y = dataset.data, dataset.target
model = RandomForestRegressor(random_state=30)
param_grid = { "n_estimators"      : [250, 300],
           "criterion"         : ["gini", "entropy"],
           "max_features"      : [3, 5],
           "max_depth"         : [10, 20],
           "min_samples_split" : [2, 4] ,
           "bootstrap": [True, False]}
grid_search = GridSearchCV(clf, param_grid, n_jobs=-1, cv=2)
grid_search.fit(X, y)
print grid_search.best_params_

Answer 1

The coarse-to-fine is actually commonly used to find the best parameters. You first start with a wide range of parameters and refined them as you get closer to the best results.

I found an awesome library which does hyperparameter optimization for scikit-learn, hyperopt-sklearn. It can auto-tune your RandomForest or any other standard classifiers. You can even auto-tune and benchmark different classifiers at the same time.

I suggest you start with that because it implements different schemes to get the best parameters:

Random Search

Tree of Parzen Estimators (TPE)

Annealing

Tree

Gaussian Process Tree

EDIT:

In the case of regression, you still need to assert if your predictions are good on the test set.

Anyways, the coarse-to-fine approach still holds and is valid for any estimator.