@@ -14,17 +14,17 @@ def plot_univariate_predictor_quality(df_auc: pd.DataFrame,
1414 Parameters
1515 ----------
1616 df_auc : pd.DatFrame
17- Contains for each variable the train auc and selection auc allong with
17+ Contains for each variable the train auc and selection auc along with
1818 a boolean indicating whether or not it is selected based on the
1919 criteria
2020 dim : tuple, optional
21- tuple with width and lentgh of the plot
21+ tuple with width and length of the plot
2222 path : str, optional
2323 path to store the figure
2424 """
2525
2626 df = (df_auc [df_auc ["preselection" ]]
27- .sort_values (by = 'AUC train ' , ascending = False ))
27+ .sort_values (by = 'AUC selection ' , ascending = False ))
2828
2929 df = pd .melt (df , id_vars = ["predictor" ],
3030 value_vars = ["AUC train" , "AUC selection" ],
@@ -60,7 +60,7 @@ def plot_correlation_matrix(df_corr: pd.DataFrame,
6060 df_corr : pd.DataFrame
6161 Correlation matrix
6262 dim : tuple, optional
63- tuple with width and lentgh of the plot
63+ tuple with width and length of the plot
6464 path : str, optional
6565 path to store the figure
6666 """
@@ -89,7 +89,7 @@ def plot_performance_curves(model_performance: pd.DataFrame,
8989 contains train-selection-validation performance for each model trained
9090 in the forward feature selection
9191 dim : tuple, optional
92- tuple with width and lentgh of the plot
92+ tuple with width and length of the plot
9393 path : str, optional
9494 path to store the figure
9595 """
@@ -141,7 +141,7 @@ def plot_variable_importance(df_variable_importance: pd.DataFrame,
141141 title : str, optional
142142 Title of the plot
143143 dim : tuple, optional
144- tuple with width and lentgh of the plot
144+ tuple with width and length of the plot
145145 path : str, optional
146146 path to store the figure
147147 """
0 commit comments