I have an imbalanced dataset and I was reading this article which looks into SMOTE and RUS to address the imbalance. So I have defined the following 3 models:
# AdaBoost ada = AdaBoostClassifier(n_estimators=100, random_state=42) ada.fit(X_train,y_train) y_pred_baseline = ada.predict(X_test) # SMOTE sm = SMOTE(random_state=42) X_train_sm, y_train_sm = sm.fit_sample(X_train, y_train) ada_sm = AdaBoostClassifier(n_estimators=100, random_state=42) ada_sm.fit(X_train_sm,y_train_sm) y_pred_sm = ada_sm.predict(X_test) #RUS rus = RandomUnderSampler(random_state=42) X_train_rus, y_train_rus = rus.fit_resample(X, y) ada_rus = AdaBoostClassifier(n_estimators=100, random_state=42) ada_rus.fit(X_train_rus,y_train_rus) y_pred_rus = ada_rus.predict(X_test)
I then plotted the precision-recall curve for these 3 models. I chose this curve as I want to visualise how the models are performing, and I am not very interested in true negatives (the negative class is the majority class).
To plot the curve, I used ScikitLearn's plot_precision_recall_curve method, like so:
from sklearn.metrics import precision_recall_curve from sklearn.metrics import plot_precision_recall_curve import matplotlib.pyplot as plt disp = plot_precision_recall_curve(ada, X_test, y_test) disp.ax_.set_title('Precision-Recall curve') disp = plot_precision_recall_curve(ada_sm, X_test, y_test) disp.ax_.set_title('Precision-Recall curve') disp = plot_precision_recall_curve(ada_rus, X_test, y_test) disp.ax_.set_title('Precision-Recall curve')
This resulted in 3 separate plots.
However, I want to have these 3 curves in one plot such that they can be easily compared. So I want a plot like the one in the article:
But I am not sure how to do this as the plot_precision_recall_curve method only takes one classifier as input.
Some help would be appreciated.