def __init__(self, n_estimators, learning_rate, min_samples_split,
min_impurity, max_depth, regression):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split
self.min_impurity = min_impurity
self.max_depth = max_depth
self.regression = regression
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
self.loss = SquareLoss() #回归树残差 -(y - y_pred)
if not self.regression: #分类树残差 y - p
self.loss = SotfMaxLoss()
#分类问题也使用回归树，利用残差去学习概率
self.trees = []
for i in range(self.n_estimators):
self.trees.append(
RegressionTree(min_samples_split=self.min_samples_split,
min_impurity=self.min_impurity,
max_depth=self.max_depth))
def __init__(self, n_estimators, learning_rate, min_samples_split,
min_impurity, max_depth, regression):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split
self.min_impurity = min_impurity
self.max_depth = max_depth
self.regression = regression
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
# Square loss for regression
# Log loss for classification
self.loss = SquareLoss()
if not self.regression:
self.loss = CrossEntropy()
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = RegressionTree(min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth)
self.trees.append(tree)
def __init__(self, n_estimators, learning_rate, min_samples_split,
min_impurity, max_depth, regression):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split
self.min_impurity = min_impurity
self.max_depth = max_depth
self.regression = regression
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
if self.regression:
self.loss = SquareLoss()
else:
self.loss = SotfMaxLoss()
# also use RegressionTree for classification problems, we study the probability
self.trees = [
RegressionTree(min_samples_split=self.min_samples_split,
min_impurity=self.min_impurity,
max_depth=self.max_depth)
for _ in range(self.n_estimators)
]