smote

问题 I am using Credit card data for oversampling using SMOTE. I am using the code written in geeksforgeeks.org (Link) After running the following code, it states something like that: print("Before OverSampling, counts of label '1': {}".format(sum(y_train == 1))) print("Before OverSampling, counts of label '0': {} \n".format(sum(y_train == 0))) # import SMOTE module from imblearn library # pip install imblearn (if you don't have imblearn in your system) from imblearn.over_sampling import SMOTE sm

问题 I am using SMOTE-NC for oversampling my categorical data. I have only 1 feature and 10500 samples. While running the below code, I am getting the error: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-151-a261c423a6d8> in <module>() 16 print(X_new.shape) # (10500, 1) 17 print(X_new) ---> 18 sm.fit_sample(X_new, Y_new) ~\AppData\Local\Continuum\Miniconda3\envs\data-science\lib\site-packages\imblearn\base.py

问题 I am new to Python and apologize in advance, if it is too simple. Cannot find anything and this question did not help. My code is # Split data y = starbucks_smote.iloc[:, -1] X = starbucks_smote.drop('label', axis = 1) # Count labels by type counter = Counter(y) print(counter) Counter({0: 9634, 1: 2895}) # Transform the dataset oversample = SMOTE() X, y = oversample.fit_resample(X, y) # Print the oversampled dataset counter = Counter(y) print(counter) Counter({0: 9634, 1: 9634}) How to save

问题 For the given imbalanced data , I have created a different pipelines for standardization & one hot encoding numeric_transformer = Pipeline(steps = [('scaler', StandardScaler())]) categorical_transformer = Pipeline(steps=['ohe', OneHotCategoricalEncoder()]) After that a column transformer keeping the above pipelines in one from sklearn.compose import ColumnTransformer preprocessor = ColumnTransformer( transformers=[ ('num', numeric_transformer, numeric_features), ('cat', categorical