def get_learner_parameters(self):
from Orange.canvas.report import plural_w
items = OrderedDict()
items["Split selection"] = self.scores[self.attribute_score][0]
items["Pruning"] = ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves", self.min_leaf),
self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes", self.min_internal),
self.limit_min_internal),
("maximum depth {}".format(self.max_depth), self.limit_depth)) if c) or "None"
return items
def get_learner_parameters(self):
from Orange.canvas.report import plural_w
items = OrderedDict()
items["Split selection"] = self.scores[self.attribute_score][0]
items["Pruning"] = ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves",
self.min_leaf), self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes",
self.min_internal), self.limit_min_internal),
("maximum depth {}".format(self.max_depth), self.limit_depth))
if c) or "None"
return items
def get_learner_parameters(self):
from Orange.canvas.report import plural_w
items = OrderedDict()
items["Pruning"] = ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves",
self.min_leaf), self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes",
self.min_internal), self.limit_min_internal),
("maximum depth {}".format(self.max_depth), self.limit_depth))
if c) or "None"
items["Binary trees"] = ("No", "Yes")[self.binary_trees]
return items
def get_learner_parameters(self):
from Orange.canvas.report import plural_w
items = OrderedDict()
items["Pruning"] = ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves",
self.min_leaf), self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes",
self.min_internal), self.limit_min_internal),
("maximum depth {}".format(self.max_depth), self.limit_depth)
) if c) or "None"
items["Binary trees"] = ("No", "Yes")[self.binary_trees]
return items
def send_report(self):
from Orange.canvas.report import plural_w
self.report_items((("Name", self.model_name), ))
self.report_items(
"Model parameters",
[("Split selection", self.scores[self.attribute_score][0]),
("Pruning", ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves",
self.min_leaf), self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes",
self.min_internal), self.limit_min_internal),
("maximum depth {}".format(self.max_depth), self.limit_depth))
if c) or "None")])
if self.data:
self.report_data("Data", self.data)
def get_learner_parameters(self):
from Orange.canvas.report import plural_w
items = OrderedDict()
items["Pruning"] = ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves",
self.min_leaf), self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes",
self.min_internal), self.limit_min_internal),
("maximum depth {}".format(self.max_depth), self.limit_depth))
if c) or "None"
if self.limit_majority:
items["Splitting"] = "Stop splitting when majority reaches %d%% " \
"(classification only)" % \
self.sufficient_majority
items["Binary trees"] = ("No", "Yes")[self.binary_trees]
return items
def send_report(self):
from Orange.canvas.report import plural_w
self.report_items((("Name", self.model_name),))
self.report_items(
"Model parameters",
[("Split selection", self.scores[self.attribute_score][0]),
("Pruning", ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves",
self.min_leaf), self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes",
self.min_internal), self.limit_min_internal),
("maximum depth {}".format(self.max_depth),
self.limit_depth))
if c) or "None")])
if self.data:
self.report_data("Data", self.data)
def get_learner_parameters(self):
from Orange.canvas.report import plural_w
items = OrderedDict()
items["Pruning"] = ", ".join(s for s, c in (
(plural_w("at least {number} instance{s} in leaves",
self.min_leaf), self.limit_min_leaf),
(plural_w("at least {number} instance{s} in internal nodes",
self.min_internal), self.limit_min_internal),
("maximum depth {}".format(self.max_depth), self.limit_depth)
) if c) or "None"
if self.limit_majority:
items["Splitting"] = "Stop splitting when majority reaches %d%% " \
"(classification only)" % \
self.sufficient_majority
items["Binary trees"] = ("No", "Yes")[self.binary_trees]
return items
