| criterion | 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 | default: "friedman_mse" |
| init | An estimator object that is used to compute the initial
predictions. ``init`` has to provide ``fit`` and ``predict``
If None it uses ``loss.init_estimator`` | default: null |
| learning_rate | learning rate shrinks the contribution of each tree by `learning_rate`
There is a trade-off between learning_rate and n_estimators | default: 0.05 |
| loss | | default: "deviance" |
| max_depth | 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 | default: 6 |
| max_features | 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 percentage 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 | default: null |
| max_leaf_nodes | 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 | default: null |
| min_impurity_split | Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf
.. versionadded:: 0.18 | default: 1e-07 |
| min_samples_leaf | The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node
.. versionchanged:: 0.18
Added float values for percentages | default: 1 |
| min_samples_split | 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 percentage and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split
.. versionchanged:: 0.18
Added float values for percentages | default: 2 |
| min_weight_fraction_leaf | 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 | default: 0.0 |
| n_estimators | The number of boosting stages to perform. Gradient boosting
is fairly robust to over-fitting so a large number usually
results in better performance | default: 50 |
| presort | 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. | default: "auto" |
| random_state | 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` | default: null |
| subsample | 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 | default: 0.5 |
| verbose | 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 | default: 0 |
| warm_start | 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 | default: false |