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Results tagged with smote
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Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models.
2
votes
Which data hyperparameter tuning using for fit the model
The code would something like this:
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import … train_test_split, RandomizedSearchCV, StratifiedKFold
X_train, y_train, X_test, y_test = train_test_split(X, y)
pipeline = Pipeline([("smote", SMOTE()), ("rf", RandomForestClassifier())])
kf = StratifiedKFold …
- 22.3k
1
vote
SMOTE and oversampling with constraints
One option would be to do something more similar to bootstrapping since that would be re-sampling existing data.
Another option would be to generate extra samples then prune based on the constraints.
- 22.3k
1
vote
How to use SMOTE in Stacking in SKLearn?
First question, whether to use SMOTE for the first or second of a stacked classifiers. … Generally, SMOTE should be done before any classification since SMOTE gives the minority class an increased likelihood be being successfully learned. …
- 22.3k
1
vote
suggestion to implement undersample and oversample
SMOTE - Synthetic Minority Over-sampling Technique is useful for oversampling.
You might want to think about dropping Class #6. 14 is not a large enough sample for machine learning. …
- 22.3k
1
vote
Unbalanced data set - how to optimize hyperparams via grid search?
Imbalanced-learn's SMOTE can also be used. If there are fewer samples than k, it will only use available samples. …
- 22.3k
1
vote
Why does class_weight usually outperform SMOTE?
SMOTE stands for Synthetic Minority Over-sampling Technique. The synthetic part means SMOTE fabricates novel samples based on existing data. … SMOTE does this by interpolating new instances based on existing feature values. …
- 22.3k
1
vote
What's the order in applying SMOTE transformation in a pipeline?
Resampling should happen after preprocessing but before the classifier.
It is best to use imblearn's Pipeline, instead of scikit-learn's Pipeline. Imblearn's Pipeline is designed to work with resampli …
- 22.3k
0
votes
Train score is very lower than Test score, is that normal?
You are probably not applying the same resampling technique to test dataset. If you put the logic into a imbalanced-learn's Pipeline, the appropriate resampling will be automatically handled for you.
- 22.3k
0
votes
Reproducible examples where balancing the training data demonstrably improves accuracy
lr = LogisticRegression(solver='liblinear', class_weight=None)
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
print(balanced_accuracy_score(y_test, y_pred))
The balanced accuracy for non-SMOTE … (X_test)
print(balanced_accuracy_score(y_test, y_pred))
The balanced accuracy for SMOTE is ~0.935. …
- 22.3k