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Methods defined here:
- __init__(self, object=None, num_levels=5, object_sequence_column=None, object_order_column=None)
- DESCRIPTION:
The DecisionForestEvaluator analyzes the model generated by the
DecisionForest and gives weights to the variables in the
model. This function shows variable/attribute counts in each tree
level, helping you to understand the importance of different
variables in the decision-making process.
PARAMETERS:
object:
Required Argument.
Specifies the teradataml DataFrame containing the model data from
DecisionForest or instance of 'DecisionForest'.
object_order_column:
Optional Argument.
Specifies Order By columns for object.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
num_levels:
Optional Argument.
Specifies number of levels to analyze.
Default Value: 5
Types: int
object_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "object". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: str OR list of Strings (str)
RETURNS:
Instance of DecisionForestEvaluator.
Output teradataml DataFrames can be accessed using attribute
references, such as DecisionForestEvaluatorObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example
load_example_data("decisionforest", "housing_train")
# Create teradataml DataFrame objects.
housing_train = DataFrame.from_table("housing_train")
# First train the data, i.e., create a decision forest model
formula = "homestyle ~ bedrooms + lotsize + gashw + driveway + stories + recroom + price + garagepl + bathrms + fullbase + airco + prefarea"
decision_forest_out = DecisionForest(data=housing_train,
formula = formula,
tree_type="classification",
nodesize=1,
variance=0.0,
max_depth=12,
maxnum_categorical=20,
mtry = 100,
seed = 100,
ntree=50
)
# Analyze output using DecisionForestEvaluator
decision_forest_eval = DecisionForestEvaluator(object=decision_forest_out,
num_levels=5
)
# Print the results.
print(decision_forest_eval.result)
- __repr__(self)
- Returns the string representation for a DecisionForestEvaluator class instance.
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