四种Python机器学习超参数搜索方法总结

# 数据读取
df = pd.read_csv('https://mirror.coggle.club/dataset/heart.csv')
X = df.drop(columns=['output'])
y = df['output']

# 数据划分
x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y)

# 模型训练与计算准确率
clf = RandomForestClassifier(random_state=0)
clf.fit(x_train, y_train)
clf.score(x_test, y_test)

parameters = {
    'max_depth': [2,4,5,6,7],
    'min_samples_leaf': [1,2,3],
    'min_weight_fraction_leaf': [0, 0.1],
    'min_impurity_decrease': [0, 0.1, 0.2]
}

# Fitting 5 folds for each of 90 candidates, totalling 450 fits
clf = GridSearchCV(
    RandomForestClassifier(random_state=0),
    parameters, refit=True, verbose=1,
)
clf.fit(x_train, y_train)
clf.best_estimator_.score(x_test, y_test)

parameters = {
    'max_depth': [2,4,5,6,7],
    'min_samples_leaf': [1,2,3],
    'min_weight_fraction_leaf': [0, 0.1],
    'min_impurity_decrease': [0, 0.1, 0.2]
}

clf = RandomizedSearchCV(
    RandomForestClassifier(random_state=0),
    parameters, refit=True, verbose=1, n_iter=10,
)

clf.fit(x_train, y_train)
clf.best_estimator_.score(x_test, y_test)

n_iterations: 3
n_required_iterations: 5
n_possible_iterations: 3
min_resources_: 20
max_resources_: 227
aggressive_elimination: False
factor: 3
----------

iter: 0
n_candidates: 90
n_resources: 20
Fitting 5 folds for each of 90 candidates, totalling 450 fits
----------

iter: 1
n_candidates: 30
n_resources: 60
Fitting 5 folds for each of 30 candidates, totalling 150 fits
----------

iter: 2
n_candidates: 10
n_resources: 180
Fitting 5 folds for each of 10 candidates, totalling 50 fits
----------

n_iterations: 3
n_required_iterations: 3
n_possible_iterations: 3
min_resources_: 20
max_resources_: 227
aggressive_elimination: False
factor: 3
----------

iter: 0
n_candidates: 11
n_resources: 20
Fitting 5 folds for each of 11 candidates, totalling 55 fits
----------

iter: 1
n_candidates: 4
n_resources: 60
Fitting 5 folds for each of 4 candidates, totalling 20 fits
----------

iter: 2
n_candidates: 2
n_resources: 180
Fitting 5 folds for each of 2 candidates, totalling 10 fits