SPBaDF

Importing the necessary libraries

from imbalanced_spdf.ensemble import SPBaDF
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score

Creating a synthetic dataset

X, y = make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=10, n_clusters_per_class=1, weights=[0.90], flip_y=0, random_state=1)

Take a look at the dataset

print(f"X shape: {X.shape}, with {X.shape[0]} samples and {X.shape[1]} features")

X shape: (100, 20), with 100 samples and 20 features

print(f"y shape: {y.shape}, with {y.shape[0]} samples, the majority class has {len(y[y==0])} samples and the minority class has {len(y[y==1])} samples")

y shape: (100,), with 100 samples, the majority class has 91 samples and the minority class has 9 samples

Splitting the dataset into training and testing

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

Fit a SPBaDF model

spbadf = SPBaDF(weight=10, n_trees=100)

spbadf.fit(X_train, y_train)

SPBaDF(n_trees=100, weight=10)

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pred = spbadf.predict(X_test)

f1_score(y_test, pred)

1.0