The Scikit-learn gradient lift algorithm (Gradient Boosting) uses

The purpose of this paper is to record the use of the GBRT in the Sklearn package, which is mainly the meaning of the official website parameters; for the theoretical part and the actual use hope in just give the source, hope to have time to add the complete

Summary:

1. example

2. Model main Parameters

3. Model main attribute variables

Content:

1. example
>>> Import NumPy as NP
>>> from sklearn.metrics import mean_squared_error
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import gradientboostingregressor

>>> X, y = make_friedman1 (n_samples=1200, random_state=0, noise=1.0)
>>> x_train, x_test = x[:200], x[200:]
>>> y_train, y_test = y[:200], y[200:]
>>> est = gradientboostingregressor (n_estimators=100, learning_rate=0.1,
... max_depth=1, random_state=0, loss= ' ls '). fit (x_train, y_train)
>>> mean_squared_error (y_test, est.predict (x_test))
5.00 ...


2. Model main Parameters
2.1 N_estimators:int (default=100)
The number of iterations of the gradient boost is also the number of weak classifiers
2.2 loss: {' ls ', ' lad ', ' Huber ', ' quantile '}, optional (default= ' ls ')
Loss function
2.3 learning_rate:float, Optional (default=0.1)
The step length of SGB (random gradient Ascension) is also called learning speed, and the lower the learning_rate, the greater the N_estimators.
Experience shows that the smaller the learning_rate, the smaller the test error; see http://scikit-learn.org/stable/modules/ensemble.html#Regularization for specific values
2.4 max_depth:integer, Optional (default=3)
Maximum depth of decision stump (decision Stump), pre-pruning Operation (tree Depth here does not include Root)
2.5 warm_start:bool, Default:false
If true, the previous fit results are stored to increase the number of iterations

3. Model main attribute variables
3.1 train_score_: array, shape = [n_estimators]
Store training errors for each iteration
3.2 feature_importances_: array, shape = [n_features]
Characteristic importance, specific reference: http://scikit-learn.org/stable/modules/ensemble.html#random-forest-feature-importance

　　

The Scikit-learn gradient lift algorithm (Gradient Boosting) uses