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Benchmark Fix for RandomForest

In the benchmark file

We use the LabelEncoder on our training and testing data separately, which will cause index mismatch.

from sklearn.preprocessing import LabelEncoder

train_preprocessed = train.apply(LabelEncoder().fit_transform)
test_preprocessed = test.apply(LabelEncoder().fit_transform)

We should change it with OrdinalEncoder

from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)

train_preprocessed = oe.fit_transform(train.fillna("NaN"))
test_preprocessed = oe.transform(test.fillna("NaN"))
train_preprocessed = pd.DataFrame(train_preprocessed, columns=train.columns)
test_preprocessed = pd.DataFrame(test_preprocessed, columns=train.columns)

With this transformation, I am able to get: 0.8526 accuracy for Random Forest.

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