- **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations
- **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations
- ``linear_tree`` :raw-html:`<a id="linear_tree" title="Permalink to this parameter" href="#linear_tree">🔗︎</a>`, default = ``false``, type = bool, aliases: ``linear_trees``
- fit piecewise linear gradient boosting tree
- tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant
- the linear model at each leaf includes all the numerical features in that leaf's branch
- categorical features are used for splits as normal but are not used in the linear models
- missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R
- it is recommended to rescale data before training so that features have similar mean and standard deviation
- **Note**: only works with CPU and ``serial`` tree learner
- **Note**: ``regression_l1`` objective is not supported with linear tree boosting
- **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM
- **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves
- ``data`` :raw-html:`<a id="data" title="Permalink to this parameter" href="#data">🔗︎</a>`, default = ``""``, type = string, aliases: ``train``, ``train_data``, ``train_data_file``, ``data_filename``
- ``data`` :raw-html:`<a id="data" title="Permalink to this parameter" href="#data">🔗︎</a>`, default = ``""``, type = string, aliases: ``train``, ``train_data``, ``train_data_file``, ``data_filename``
- path of training data, LightGBM will train from this data
- path of training data, LightGBM will train from this data
...
@@ -672,6 +650,28 @@ IO Parameters
...
@@ -672,6 +650,28 @@ IO Parameters
Dataset Parameters
Dataset Parameters
~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~
- ``linear_tree`` :raw-html:`<a id="linear_tree" title="Permalink to this parameter" href="#linear_tree">🔗︎</a>`, default = ``false``, type = bool, aliases: ``linear_trees``
- fit piecewise linear gradient boosting tree
- tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant
- the linear model at each leaf includes all the numerical features in that leaf's branch
- categorical features are used for splits as normal but are not used in the linear models
- missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R
- it is recommended to rescale data before training so that features have similar mean and standard deviation
- **Note**: only works with CPU and ``serial`` tree learner
- **Note**: ``regression_l1`` objective is not supported with linear tree boosting
- **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM
- **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves
- ``max_bin`` :raw-html:`<a id="max_bin" title="Permalink to this parameter" href="#max_bin">🔗︎</a>`, default = ``255``, type = int, constraints: ``max_bin > 1``
- ``max_bin`` :raw-html:`<a id="max_bin" title="Permalink to this parameter" href="#max_bin">🔗︎</a>`, default = ``255``, type = int, constraints: ``max_bin > 1``
- max number of bins that feature values will be bucketed in
- max number of bins that feature values will be bucketed in
// descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations
// descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations
std::stringboosting="gbdt";
std::stringboosting="gbdt";
// alias = linear_trees
// desc = fit piecewise linear gradient boosting tree
// descl2 = tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant
// descl2 = the linear model at each leaf includes all the numerical features in that leaf's branch
// descl2 = categorical features are used for splits as normal but are not used in the linear models
// descl2 = missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R
// descl2 = it is recommended to rescale data before training so that features have similar mean and standard deviation
// descl2 = **Note**: only works with CPU and ``serial`` tree learner
// descl2 = **Note**: ``regression_l1`` objective is not supported with linear tree boosting
// descl2 = **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM
// descl2 = **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves
boollinear_tree=false;
// alias = train, train_data, train_data_file, data_filename
// alias = train, train_data, train_data_file, data_filename
// desc = path of training data, LightGBM will train from this data
// desc = path of training data, LightGBM will train from this data
// desc = **Note**: can be used only in CLI version
// desc = **Note**: can be used only in CLI version
...
@@ -586,6 +573,19 @@ struct Config {
...
@@ -586,6 +573,19 @@ struct Config {
#pragma region Dataset Parameters
#pragma region Dataset Parameters
// alias = linear_trees
// desc = fit piecewise linear gradient boosting tree
// descl2 = tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant
// descl2 = the linear model at each leaf includes all the numerical features in that leaf's branch
// descl2 = categorical features are used for splits as normal but are not used in the linear models
// descl2 = missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R
// descl2 = it is recommended to rescale data before training so that features have similar mean and standard deviation
// descl2 = **Note**: only works with CPU and ``serial`` tree learner
// descl2 = **Note**: ``regression_l1`` objective is not supported with linear tree boosting
// descl2 = **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM
// descl2 = **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves
boollinear_tree=false;
// check = >1
// check = >1
// desc = max number of bins that feature values will be bucketed in
// desc = max number of bins that feature values will be bucketed in
// desc = small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)
// desc = small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)
