RandomForestLearner
RandomForestLearner
RandomForestLearner(label: str, task: Task = generic_learner.Task.CLASSIFICATION, weights: Optional[str] = None, ranking_group: Optional[str] = None, uplift_treatment: Optional[str] = None, features: ColumnDefs = None, include_all_columns: bool = False, max_vocab_count: int = 2000, min_vocab_frequency: int = 5, discretize_numerical_columns: bool = False, num_discretized_numerical_bins: int = 255, max_num_scanned_rows_to_infer_semantic: int = 10000, max_num_scanned_rows_to_compute_statistics: int = 10000, data_spec: Optional[DataSpecification] = None, adapt_bootstrap_size_ratio_for_maximum_training_duration: Optional[bool] = False, allow_na_conditions: Optional[bool] = False, bootstrap_size_ratio: Optional[float] = 1.0, bootstrap_training_dataset: Optional[bool] = True, categorical_algorithm: Optional[str] = 'CART', categorical_set_split_greedy_sampling: Optional[float] = 0.1, categorical_set_split_max_num_items: Optional[int] = 1, categorical_set_split_min_item_frequency: Optional[int] = 1, compute_oob_performances: Optional[bool] = True, compute_oob_variable_importances: Optional[bool] = False, growing_strategy: Optional[str] = 'LOCAL', honest: Optional[bool] = False, honest_fixed_separation: Optional[bool] = False, honest_ratio_leaf_examples: Optional[float] = 0.5, in_split_min_examples_check: Optional[bool] = True, keep_non_leaf_label_distribution: Optional[bool] = True, max_depth: Optional[int] = 16, max_num_nodes: Optional[int] = None, maximum_model_size_in_memory_in_bytes: Optional[float] = 1.0, maximum_training_duration_seconds: Optional[float] = 1.0, mhld_oblique_max_num_attributes: Optional[int] = None, mhld_oblique_sample_attributes: Optional[bool] = None, min_examples: Optional[int] = 5, missing_value_policy: Optional[str] = 'GLOBAL_IMPUTATION', num_candidate_attributes: Optional[int] = 0, num_candidate_attributes_ratio: Optional[float] = 1.0, num_oob_variable_importances_permutations: Optional[int] = 1, num_trees: Optional[int] = 300, pure_serving_model: Optional[bool] = False, random_seed: Optional[int] = 123456, sampling_with_replacement: Optional[bool] = True, sorting_strategy: Optional[str] = 'PRESORT', sparse_oblique_max_num_projections: Optional[int] = None, sparse_oblique_normalization: Optional[str] = None, sparse_oblique_num_projections_exponent: Optional[float] = None, sparse_oblique_projection_density_factor: Optional[float] = None, sparse_oblique_weights: Optional[str] = None, split_axis: Optional[str] = 'AXIS_ALIGNED', uplift_min_examples_in_treatment: Optional[int] = 5, uplift_split_score: Optional[str] = 'KULLBACK_LEIBLER', winner_take_all: Optional[bool] = True, num_threads: Optional[int] = None, working_dir: Optional[str] = None, resume_training: bool = False, resume_training_snapshot_interval_seconds: int = 1800, tuner: Optional[AbstractTuner] = None, workers: Optional[Sequence[str]] = None)
Bases: GenericLearner
Random Forest learning algorithm.
A Random Forest (https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) is a collection of deep CART decision trees trained independently and without pruning. Each tree is trained on a random subset of the original training dataset (sampled with replacement).
The algorithm is unique in that it is robust to overfitting, even in extreme cases e.g. when there are more features than training examples.
It is probably the most wellknown of the Decision Forest training algorithms.
Usage example:
import ydf
import pandas as pd
dataset = pd.read_csv("project/dataset.csv")
model = ydf.RandomForestLearner().train(dataset)
print(model.summary())
Hyperparameters are configured to give reasonable results for typical
datasets. Hyperparameters can also be modified manually (see descriptions)
below or by applying the hyperparameter templates available with
RandomForestLearner.hyperparameter_templates()
(see this function's documentation for
details).
Attributes:
Name  Type  Description 

label 
Label of the dataset. The label column
should not be identified as a feature in the 

task 
Task to solve (e.g. Task.CLASSIFICATION, Task.REGRESSION, Task.RANKING, Task.CATEGORICAL_UPLIFT, Task.NUMERICAL_UPLIFT). 

weights 
Name of a feature that identifies the weight of each example. If
weights are not specified, unit weights are assumed. The weight column
should not be identified as a feature in the 

ranking_group 
Only for 

uplift_treatment 
Only for 

features 
If None, all columns are used as features. The semantic of the
features is determined automatically. Otherwise, if
include_all_columns=False (default) only the column listed in 

include_all_columns 
See 

max_vocab_count 
Maximum size of the vocabulary of CATEGORICAL and CATEGORICAL_SET columns stored as strings. If more unique values exist, only the most frequent values are kept, and the remaining values are considered as outofvocabulary. 

min_vocab_frequency 
Minimum number of occurrence of a value for CATEGORICAL
and CATEGORICAL_SET columns. Value observed less than


discretize_numerical_columns 
If true, discretize all the numerical columns
before training. Discretized numerical columns are faster to train with,
but they can have a negative impact on the model quality. Using


num_discretized_numerical_bins 
Number of bins used when disretizing numerical columns. 

max_num_scanned_rows_to_infer_semantic 
Number of rows to scan when inferring the column's semantic if it is not explicitly specified. Only used when reading from file, inmemory datasets are always read in full. Setting this to a lower number will speed up dataset reading, but might result in incorrect column semantics. Set to 1 to scan the entire dataset. 

max_num_scanned_rows_to_compute_statistics 
Number of rows to scan when computing a column's statistics. Only used when reading from file, inmemory datasets are always read in full. A column's statistics include the dictionary for categorical features and the mean / min / max for numerical features. Setting this to a lower number will speed up dataset reading, but skew statistics in the dataspec, which can hurt model quality (e.g. if an important category of a categorical feature is considered OOV). Set to 1 to scan the entire dataset. 

data_spec 
Dataspec to be used (advanced). If a data spec is given,


adapt_bootstrap_size_ratio_for_maximum_training_duration 
Control how the
maximum training duration (if set) is applied. If false, the training
stop when the time is used. If true, adapts the size of the sampled
dataset used to train each tree such that 

allow_na_conditions 
If true, the tree training evaluates conditions of the
type 

bootstrap_size_ratio 
Number of examples used to train each trees; expressed as a ratio of the training dataset size. Default: 1.0. 

bootstrap_training_dataset 
If true (default), each tree is trained on a separate dataset sampled with replacement from the original dataset. If false, all the trees are trained on the entire same dataset. If bootstrap_training_dataset:false, OOB metrics are not available. bootstrap_training_dataset=false is used in "Extremely randomized trees" (https://link.springer.com/content/pdf/10.1007%2Fs1099400662261.pdf). Default: True. 

categorical_algorithm 
How to learn splits on categorical attributes.
 

categorical_set_split_greedy_sampling 
For categorical set splits e.g. texts. Probability for a categorical value to be a candidate for the positive set. The sampling is applied once per node (i.e. not at every step of the greedy optimization). Default: 0.1. 

categorical_set_split_max_num_items 
For categorical set splits e.g. texts.
Maximum number of items (prior to the sampling). If more items are
available, the least frequent items are ignored. Changing this value is
similar to change the "max_vocab_count" before loading the dataset, with
the following exception: With 

categorical_set_split_min_item_frequency 
For categorical set splits e.g. texts. Minimum number of occurrences of an item to be considered. Default: 1. 

compute_oob_performances 
If true, compute the Outofbag evaluation (then available in the summary and model inspector). This evaluation is a cheap alternative to crossvalidation evaluation. Default: True. 

compute_oob_variable_importances 
If true, compute the Outofbag feature importance (then available in the summary and model inspector). Note that the OOB feature importance can be expensive to compute. Default: False. 

growing_strategy 
How to grow the tree.
 

honest 
In honest trees, different training examples are used to infer the structure and the leaf values. This regularization technique trades examples for bias estimates. It might increase or reduce the quality of the model. See "Generalized Random Forests", Athey et al. In this paper, Honest trees are trained with the Random Forest algorithm with a sampling without replacement. Default: False. 

honest_fixed_separation 
For honest trees only i.e. honest=true. If true, a new random separation is generated for each tree. If false, the same separation is used for all the trees (e.g., in Gradient Boosted Trees containing multiple trees). Default: False. 

honest_ratio_leaf_examples 
For honest trees only i.e. honest=true. Ratio of examples used to set the leaf values. Default: 0.5. 

in_split_min_examples_check 
Whether to check the 

keep_non_leaf_label_distribution 
Whether to keep the node value (i.e. the distribution of the labels of the training examples) of nonleaf nodes. This information is not used during serving, however it can be used for model interpretation as well as hyper parameter tuning. This can take lots of space, sometimes accounting for half of the model size. Default: True. 

max_depth 
Maximum depth of the tree. 

max_num_nodes 
Maximum number of nodes in the tree. Set to 1 to disable
this limit. Only available for 

maximum_model_size_in_memory_in_bytes 
Limit the size of the model when stored in ram. Different algorithms can enforce this limit differently. Note that when models are compiled into an inference, the size of the inference engine is generally much smaller than the original model. Default: 1.0. 

maximum_training_duration_seconds 
Maximum training duration of the model expressed in seconds. Each learning algorithm is free to use this parameter at it sees fit. Enabling maximum training duration makes the model training nondeterministic. Default: 1.0. 

mhld_oblique_max_num_attributes 
For MHLD oblique splits i.e.


mhld_oblique_sample_attributes 
For MHLD oblique splits i.e.


min_examples 
Minimum number of examples in a node. Default: 5. 

missing_value_policy 
Method used to handle missing attribute values.
 

num_candidate_attributes 
Number of unique valid attributes tested for each
node. An attribute is valid if it has at least a valid split. If


num_candidate_attributes_ratio 
Ratio of attributes tested at each node. If
set, it is equivalent to 

num_oob_variable_importances_permutations 
Number of time the dataset is reshuffled to compute the permutation variable importances. Increasing this value increase the training time (if "compute_oob_variable_importances:true") as well as the stability of the oob variable importance metrics. Default: 1. 

num_trees 
Number of individual decision trees. Increasing the number of trees can increase the quality of the model at the expense of size, training speed, and inference latency. Default: 300. 

pure_serving_model 
Clear the model from any information that is not required for model serving. This includes debugging, model interpretation and other metadata. The size of the serialized model can be reduced significatively (50% model size reduction is common). This parameter has no impact on the quality, serving speed or RAM usage of model serving. Default: False. 

random_seed 
Random seed for the training of the model. Learners are expected to be deterministic by the random seed. Default: 123456. 

sampling_with_replacement 
If true, the training examples are sampled with replacement. If false, the training samples are sampled without replacement. Only used when "bootstrap_training_dataset=true". If false (sampling without replacement) and if "bootstrap_size_ratio=1" (default), all the examples are used to train all the trees (you probably do not want that). Default: True. 

sorting_strategy 
How are sorted the numerical features in order to find the splits  PRESORT: The features are presorted at the start of the training. This solution is faster but consumes much more memory than IN_NODE.  IN_NODE: The features are sorted just before being used in the node. This solution is slow but consumes little amount of memory. . Default: "PRESORT". 

sparse_oblique_max_num_projections 
For sparse oblique splits i.e.


sparse_oblique_normalization 
For sparse oblique splits i.e.


sparse_oblique_num_projections_exponent 
For sparse oblique splits i.e.


sparse_oblique_projection_density_factor 
Density of the projections as an
exponent of the number of features. Independently for each projection,
each feature has a probability "projection_density_factor / num_features"
to be considered in the projection.
The paper "Sparse Projection Oblique Random Forests" (Tomita et al, 2020)
calls this parameter 

sparse_oblique_weights 
For sparse oblique splits i.e.


split_axis 
What structure of split to consider for numerical features.
 

uplift_min_examples_in_treatment 
For uplift models only. Minimum number of examples per treatment in a node. Default: 5. 

uplift_split_score 
For uplift models only. Splitter score i.e. score
optimized by the splitters. The scores are introduced in "Decision trees
for uplift modeling with single and multiple treatments", Rzepakowski et
al. Notation: 

winner_take_all 
Control how classification trees vote. If true, each tree votes for one class. If false, each tree vote for a distribution of classes. winner_take_all_inference=false is often preferable. Default: True. 

num_threads 
Number of threads used to train the model. Different learning
algorithms use multithreading differently and with different degree of
efficiency. If 

resume_training 
If true, the model training resumes from the checkpoint
stored in the 

working_dir 
Path to a directory available for the learning algorithm to store intermediate computation results. Depending on the learning algorithm and parameters, the working_dir might be optional, required, or ignored. For instance, distributed training algorithm always need a "working_dir", and the gradient boosted tree and hyperparameter tuners will export artefacts to the "working_dir" if provided. 

resume_training_snapshot_interval_seconds 
Indicative number of seconds in
between snapshots when 

tuner 
If set, automatically select the best hyperparameters using the provided tuner. When using distributed training, the tuning is distributed. 

workers 
If set, enable distributed training. "workers" is the list of IP
addresses of the workers. A worker is a process running

cross_validation
cross_validation(ds: InputDataset, folds: int = 10, bootstrapping: Union[bool, int] = False, parallel_evaluations: int = 1) > Evaluation
Crossvalidates the learner and return the evaluation.
Usage example:
import pandas as pd
import ydf
dataset = pd.read_csv("my_dataset.csv")
learner = ydf.RandomForestLearner(label="label")
evaluation = learner.cross_validation(dataset)
# In a notebook, display an interractive evaluation
evaluation
# Print the evaluation
print(evaluation)
# Look at specific metrics
print(evaluation.accuracy)
Parameters:
Name  Type  Description  Default 

ds 
InputDataset

Dataset for the crossvalidation. 
required 
folds 
int

Number of crossvalidation folds. 
10

bootstrapping 
Union[bool, int]

Controls whether bootstrapping is used to evaluate the confidence intervals and statistical tests (i.e., all the metrics ending with "[B]"). If set to false, bootstrapping is disabled. If set to true, bootstrapping is enabled and 2000 bootstrapping samples are used. If set to an integer, it specifies the number of bootstrapping samples to use. In this case, if the number is less than 100, an error is raised as bootstrapping will not yield useful results. 
False

parallel_evaluations 
int

Number of model to train and evaluate in parallel
using multithreading. Note that each model is potentially already
trained with multithreading (see 
1

Returns:
Type  Description 

Evaluation

The crossvalidation evaluation. 
hyperparameter_templates
classmethod
Hyperparameter templates for this Learner.
Hyperparameter templates are sets of predefined hyperparameters for easy access to different variants of the learner. Each template is a mapping to a set of hyperparameters and can be applied directly on the learner.
Usage example:
templates = ydf.RandomForestLearner.hyperparameter_templates()
better_defaultv1 = templates["better_defaultv1"]
# Print a description of the template
print(better_defaultv1.description)
# Apply the template's settings on the learner.
learner = ydf.RandomForestLearner(label, **better_defaultv1)
Returns:
Type  Description 

Dict[str, HyperparameterTemplate]

Dictionary of the available templates 
train
train(ds: InputDataset, valid: Optional[InputDataset] = None) > RandomForestModel
Trains a model on the given dataset.
Options for dataset reading are given on the learner. Consult the documentation of the learner or ydf.create_vertical_dataset() for additional information on dataset reading in YDF.
Usage example:
import ydf
import pandas as pd
train_ds = pd.read_csv(...)
learner = ydf.RandomForestLearner(label="label")
model = learner.train(train_ds)
print(model.summary())
If training is interrupted (for example, by interrupting the cell execution in Colab), the model will be returned to the state it was in at the moment of interruption.
Parameters:
Name  Type  Description  Default 

ds 
InputDataset

Training dataset. 
required 
valid 
Optional[InputDataset]

Optional validation dataset. Some learners, such as Random Forest, do not need validation dataset. Some learners, such as GradientBoostedTrees, automatically extract a validation dataset from the training dataset if the validation dataset is not provided. 
None

Returns:
Type  Description 

RandomForestModel

A trained model. 