def __init__(self,
n_estimators=200,
learning_rate=0.001,
min_samples_split=2,
min_impurity=1e-7,
max_depth=2):
self.n_estimators = n_estimators # Number of trees
self.learning_rate = learning_rate # Step size for weight update
self.min_samples_split = min_samples_split # The minimum n of sampels to justify split
self.min_impurity = min_impurity # Minimum variance reduction to continue
self.max_depth = max_depth # Maximum depth for tree
# self.bar = progressbar.ProgressBar(widgets=bar_widgets)
# Log loss for classification
self.loss = LogisticLoss()
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = XGBoostRegressionTree(
min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth,
loss=self.loss)
self.trees.append(tree)
def __init__(self,
n_estimators=200,
learning_rate=0.001,
min_samples_split=2,
min_impurity=1e-7,
max_depth=2,
debug=False):
self.n_estimators = n_estimators # Number of trees
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split # The minimum n of sampels to justify split
self.min_impurity = min_impurity # Minimum variance reduction to continue
self.max_depth = max_depth # Maximum depth for tree
self.init_estimate = None # The initial prediction of y
self.debug = debug
# Log loss for classification
self.loss = LogisticLoss()
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = XGBoostRegressionTree(
min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth,
loss=self.loss)
self.trees.append(tree)