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fix some typos and consistency #387

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6 changes: 3 additions & 3 deletions libmultilabel/linear/linear.py
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ def train_1vsrest(
options: str = "",
verbose: bool = True,
) -> FlatModel:
"""Trains a linear model for multiabel data using a one-vs-rest strategy.
"""Trains a linear model for multi-label data using a one-vs-rest strategy.

Args:
y (sparse.csr_matrix): A 0/1 matrix with dimensions number of instances * number of classes.
Expand Down Expand Up @@ -396,7 +396,7 @@ def train_cost_sensitive(
options: str = "",
verbose: bool = True,
) -> FlatModel:
"""Trains a linear model for multilabel data using a one-vs-rest strategy
"""Trains a linear model for multi-label data using a one-vs-rest strategy
and cross-validation to pick an optimal asymmetric misclassification cost
for Macro-F1.
Outperforms train_1vsrest in most aspects at the cost of higher
Expand Down Expand Up @@ -500,7 +500,7 @@ def train_cost_sensitive_micro(
options: str = "",
verbose: bool = True,
) -> FlatModel:
"""Trains a linear model for multilabel data using a one-vs-rest strategy
"""Trains a linear model for multi-label data using a one-vs-rest strategy
and cross-validation to pick an optimal asymmetric misclassification cost
for Micro-F1.
Outperforms train_1vsrest in most aspects at the cost of higher
Expand Down
2 changes: 1 addition & 1 deletion libmultilabel/linear/tree.py
Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,7 @@ def train_tree(
dmax=10,
verbose: bool = True,
) -> TreeModel:
"""Trains a linear model for multiabel data using a divide-and-conquer strategy.
"""Trains a linear model for multi-label data using a divide-and-conquer strategy.
The algorithm used is based on https://github.com/xmc-aalto/bonsai.

Args:
Expand Down
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