In the last cell of 14_machine_learning/17_boosting_algorithms.ipynb, where bar plots are created for metrices of Decision Tree, Random Forest Tree, and XG Boost, all values for y_test and y_pred are as per XGBClassifier algorithm, due to which all sub-bar plots are same, i.e, values are around 0.80.
While, values for Decision Tree, Random Forest Tree, and XG Boost are 0.71, 0.78, and 0.80, respectively.
If we put the respective values of metrices from all algorithms, then the bar plot should be like this:
ylim for these plots is (0, 0.85)
In the last cell of 14_machine_learning/17_boosting_algorithms.ipynb, where bar plots are created for metrices of Decision Tree, Random Forest Tree, and XG Boost, all values for y_test and y_pred are as per XGBClassifier algorithm, due to which all sub-bar plots are same, i.e, values are around 0.80.
While, values for Decision Tree, Random Forest Tree, and XG Boost are 0.71, 0.78, and 0.80, respectively.
If we put the respective values of metrices from all algorithms, then the bar plot should be like this:
ylim for these plots is (0, 0.85)