The F1Score is: {f1Score:<5.3f} = {f1Score*100:<5.1f}%
""")
"""=============================================================================================="""
good = []
bad = []
for i in dataset["Liked"]:
if i == 1:
good.append(i)
elif i == 0:
bad.append(i)
print(f"""Se tuvieron {len(good)} reseñas buenas.
Y se tuvieron {len(bad)} reseñas malas.""")
from nltk.classify import maxent
train = [
({'a': 1, 'b': 1, 'c': 1}, 'y'),
({'a': 5, 'b': 5, 'c': 5}, 'x'),
({'a': 0.9, 'b': 0.9, 'c': 0.9}, 'y'),
({'a': 5.5, 'b': 5.4, 'c': 5.3}, 'x'),
({'a': 0.8, 'b': 1.2, 'c': 1}, 'y'),
({'a': 5.1, 'b': 4.9, 'c': 5.2}, 'x')
]
test = [
{'a': 1, 'b': 0.8, 'c': 1.2},
{'a': 5.2, 'b': 5.1, 'c': 5}
]
encoding = maxent.TypedMaxentFeatureEncoding.train(train, count_cutoff=3, alwayson_features=True)
classifier = maxent.MaxentClassifier.train(train, bernoulli=False, encoding=encoding, trace=0)
classifier.classify_many(test)
['y', 'x']
