diff --git a/feature/linear.py b/feature/linear.py
index c931867..7561f45 100644
--- a/feature/linear.py
+++ b/feature/linear.py
@@ -4,9 +4,11 @@
 
 from typing import NoReturn, Tuple
 
+import numpy as np
 import pandas as pd
 from sklearn.linear_model import LinearRegression, Lasso, Ridge
 from sklearn.linear_model import LogisticRegression, RidgeClassifier
+from sklearn.multiclass import OneVsRestClassifier
 
 from feature.base import _BaseSupervisedSelector, _BaseDispatcher
 from feature.utils import Num, get_task_string
@@ -29,10 +31,10 @@ def __init__(self, seed: int, num_features: Num, regularization: str, alpha:Num)
                         "regression_lasso": Lasso(random_state=self.seed),
                         "regression_ridge": Ridge(random_state=self.seed),
                         # "classification_none": LogisticRegression(penalty="none"), # won't converge most times
-                        "classification_none": LogisticRegression(random_state=self.seed,
-                                                                  multi_class="auto", solver="liblinear"),
-                        "classification_lasso": LogisticRegression(random_state=self.seed, penalty='l1',
-                                                                   multi_class="auto", solver="liblinear"),
+                        "classification_none": OneVsRestClassifier(
+                            LogisticRegression(random_state=self.seed, solver="liblinear")),
+                        "classification_lasso": OneVsRestClassifier(
+                            LogisticRegression(random_state=self.seed, penalty='l1', solver="liblinear")),
                         "classification_ridge": RidgeClassifier(random_state=self.seed)}
 
     def get_model_args(self, selection_method) -> Tuple:
@@ -60,13 +62,18 @@ def fit(self, data: pd.DataFrame, labels: pd.Series) -> NoReturn:
         # But that does not necessarily mean they are more important
         # See more discussion here:
         # https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py
-        self.abs_scores = abs(self.imp.coef_)
+        if isinstance(self.imp, OneVsRestClassifier):
+            coefficients = np.vstack([estimator.coef_ for estimator in self.imp.estimators_])
+        else:
+            coefficients = self.imp.coef_
 
         # LogisticRegression/RidgeClassifier returns a coef_ array of (n_classes, n_features)
         # These coefficients map the importance of the feature for a specific class.
         # One approach is to average the importances
-        if isinstance(self.imp, LogisticRegression) or isinstance(self.imp, RidgeClassifier):
-            self.abs_scores = abs(self.imp.coef_.mean(0))
+        if isinstance(self.imp, (OneVsRestClassifier, LogisticRegression, RidgeClassifier)):
+            self.abs_scores = abs(coefficients.mean(0))
+        else:
+            self.abs_scores = abs(coefficients)
 
     def transform(self, data: pd.DataFrame) -> pd.DataFrame: