17416
1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier
sklearn.GradientBoostingClassifier
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier
23
openml==0.10.2,sklearn==0.21.2
Gradient Boosting for classification.
GB builds an additive model in a
forward stage-wise fashion; it allows for the optimization of
arbitrary differentiable loss functions. In each stage ``n_classes_``
regression trees are fit on the negative gradient of the
binomial or multinomial deviance loss function. Binary classification
is a special case where only a single regression tree is induced.
2019-11-22T01:27:50
English
sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9
criterion
string
"friedman_mse"
The function to measure the quality of a split. Supported criteria
are "friedman_mse" for the mean squared error with improvement
score by Friedman, "mse" for mean squared error, and "mae" for
the mean absolute error. The default value of "friedman_mse" is
generally the best as it can provide a better approximation in
some cases
.. versionadded:: 0.18
init
estimator or
null
An estimator object that is used to compute the initial predictions
``init`` has to provide `fit` and `predict_proba`. If 'zero', the
initial raw predictions are set to zero. By default, a
``DummyEstimator`` predicting the classes priors is used
learning_rate
float
0.1
learning rate shrinks the contribution of each tree by `learning_rate`
There is a trade-off between learning_rate and n_estimators
loss
"deviance"
max_depth
integer
3
maximum depth of the individual regression estimators. The maximum
depth limits the number of nodes in the tree. Tune this parameter
for best performance; the best value depends on the interaction
of the input variables
max_features
int
null
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split
- If float, then `max_features` is a fraction and
`int(max_features * n_features)` features are considered at each
split
- If "auto", then `max_features=sqrt(n_features)`
- If "sqrt", then `max_features=sqrt(n_features)`
- If "log2", then `max_features=log2(n_features)`
- If None, then `max_features=n_features`
Choosing `max_features < n_features` leads to a reduction of variance
and an increase in bias
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features
max_leaf_nodes
int or None
null
Grow trees with ``max_leaf_nodes`` in best-first fashion
Best nodes are defined as relative reduction in impurity
If None then unlimited number of leaf nodes
min_impurity_decrease
float
0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed
.. versionadded:: 0.19
min_impurity_split
float
null
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead
min_samples_leaf
int
1
The minimum number of samples required to be at a leaf node
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression
- If int, then consider `min_samples_leaf` as the minimum number
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node
.. versionchanged:: 0.18
Added float values for fractions
min_samples_split
int
2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split
.. versionchanged:: 0.18
Added float values for fractions
min_weight_fraction_leaf
float
0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided
n_estimators
int
100
The number of boosting stages to perform. Gradient boosting
is fairly robust to over-fitting so a large number usually
results in better performance
n_iter_no_change
int
null
``n_iter_no_change`` is used to decide if early stopping will be used
to terminate training when validation score is not improving. By
default it is set to None to disable early stopping. If set to a
number, it will set aside ``validation_fraction`` size of the training
data as validation and terminate training when validation score is not
improving in all of the previous ``n_iter_no_change`` numbers of
iterations. The split is stratified
.. versionadded:: 0.20
presort
bool or
"auto"
Whether to presort the data to speed up the finding of best splits in
fitting. Auto mode by default will use presorting on dense data and
default to normal sorting on sparse data. Setting presort to true on
sparse data will raise an error
.. versionadded:: 0.17
*presort* parameter
random_state
int
0
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`
subsample
float
1.0
The fraction of samples to be used for fitting the individual base
learners. If smaller than 1.0 this results in Stochastic Gradient
Boosting. `subsample` interacts with the parameter `n_estimators`
Choosing `subsample < 1.0` leads to a reduction of variance
and an increase in bias
tol
float
0.0001
Tolerance for the early stopping. When the loss is not improving
by at least tol for ``n_iter_no_change`` iterations (if set to a
number), the training stops
.. versionadded:: 0.20
validation_fraction
float
0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1
Only used if ``n_iter_no_change`` is set to an integer
.. versionadded:: 0.20
verbose
int
0
Enable verbose output. If 1 then it prints progress and performance
once in a while (the more trees the lower the frequency). If greater
than 1 then it prints progress and performance for every tree
warm_start
bool
false
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just erase the
previous solution. See :term:`the Glossary <warm_start>`
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2