relevance = resreg.sigmoid_relevance(y_train, cl=cl, ch=ch) X_train, y_train = resreg.random_oversample(X_train, y_train, relevance, relevance_threshold=0.5, over=sample_method, random_state=rrr) reg.fit(X_train, y_train) elif strategy == 'SMOTER': cl, ch, sample_method, k = param relevance = resreg.sigmoid_relevance(y_train, cl=cl, ch=ch) X_train, y_train = resreg.smoter(X_train, y_train, relevance, relevance_threshold=0.5, k=k, over=sample_method, random_state=rrr) reg.fit(X_train, y_train) elif strategy == 'GN': cl, ch, sample_method, delta = param relevance = resreg.sigmoid_relevance(y_train, cl=cl, ch=ch) X_train, y_train = resreg.gaussian_noise(X_train, y_train, relevance, relevance_threshold=0.5, delta=delta, over=sample_method, random_state=rrr)
relevance_threshold=0.5, over='balance', random_state=0) sns.kdeplot(y_train, bw=7, linewidth=lw, label=strategy, color='blue', linestyle=style2) elif strategy == 'SMOTER': relevance = resreg.sigmoid_relevance(y_train, cl=None, ch=60) X_train, y_train = resreg.smoter(X_train, y_train, relevance=relevance, relevance_threshold=0.5, k=10, over='average', random_state=0) sns.kdeplot(y_train, bw=7, linewidth=lw, label=strategy, color='red', linestyle=style2) elif strategy == 'GN': relevance = resreg.sigmoid_relevance(y_train, cl=None, ch=72.2) X_train, y_train = resreg.gaussian_noise(X_train, y_train, relevance=relevance,