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,
