31_evaluate_imbalanced_model.py

# Precision = TruePositives / (TruePositives + FalsePositives)
# Recall = TruePositives / (TruePositives + FalseNegatives)
# F-measure = (2 * Precision * Recall) / (Precision + Recall)


# evaluate imbalanced classification model with different metrics
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
# generate dataset
X, y = make_classification(n_samples=10000, n_features=2, n_redundant=0, n_clusters_per_class=1, weights=[0.99], flip_y=0)
# split into train/test sets with same class ratio
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, stratify=y)
# define model
model = LogisticRegression(solver='liblinear')
# fit model
model.fit(trainX, trainy)
# predict on test set
yhat = model.predict(testX)
# evaluate predictions
print('Accuracy: %.3f' % accuracy_score(testy, yhat))
print('Precision: %.3f' % precision_score(testy, yhat))
print('Recall: %.3f' % recall_score(testy, yhat))
print('F-measure: %.3f' % f1_score(testy, yhat))