I have an imbalanced dataset like so:

df['y'].value_counts(normalize=True) * 100
No     92.769441
Yes     7.230559
Name: y, dtype: float64

The dataset consists of 13194 rows and 37 features.

I have tried numerous attempts to improve the performance of my models by oversampling and undersampling to balance the data, One Class SVM for outlier detection, using different scores, hyperparametre tuning, etc. Some of these methods have improved the performance slightly, but not as much as I would like:

Applying RandomUnderSampling:

from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(random_state=42)
X_train_rus, y_train_rus = rus.fit_resample(X_train, y_train)

# Define and fit AdaBoost classifier using undersampled data 
ada_rus = AdaBoostClassifier(n_estimators=100, random_state=42)
y_pred_rus = ada_rus.predict(X_test) 

evaluate_model(y_test, y_pred_rus)

enter image description here

Using Oversampling techniques such as SMOTE:

from imblearn.over_sampling import SMOTE

# upsample minority class using SMOTE
sm = SMOTE(random_state=42)
X_train_sm, y_train_sm = sm.fit_sample(X_train, y_train)

# Define and fit AdaBoost classifier using upsample data 
ada_sm = AdaBoostClassifier(n_estimators=100, random_state=42)
y_pred_sm = ada_sm.predict(X_test) 

# compare predicted outcome through AdaBoost upsampled data with real outcome
evaluate_model(y_test, y_pred_sm)

enter image description here

I then decided to attempt removing rows with missing data from samples from the majority class as I saw this in an article. I did this gradually by increasing the threshold (thresh) parameter in pandas dropna function, and each time I removed more rows, the performance improved. Finally, I removed all rows from the majority class with missing data like so:

df_majority_droppedRows = df.query("y == 'No'").dropna()
df_minority = df.query("y == 'Yes'")
dfWithDroppedRows = pd.concat([df_majority_droppedRows, df_minority])
(1632, 37)

This reduced the number of rows I have dramatically down to 1632 and changed the distribution in the target variable such that what was perviously the minority class('Yes') was now the majority class:

Yes    58.455882
No     41.544118
Name: y, dtype: float64

Testing the model, I found it performed best, with high recall and precision values.

enter image description here

So my questions are,

  1. Why did this method outperform other oversampling and undersampling techniques?

  2. Is it acceptable that what was previously a minority class is now the majority class or can this cause overfitting?

  3. Is it realistic to build a model that relies on input with no missing data for the majority class samples?


In response to the questions in the comment by @Ben Reiniger:

  • I dealt with the missing values like in the data by using KNNImputer for numeric data and SimpleImputer for categorical data like so:

def preprocess (X):
    # define categorical and numeric transformers
    numeric_transformer = Pipeline(steps=[
        ('knnImputer', KNNImputer(n_neighbors=2, weights="uniform")),
        ('scaler', StandardScaler())])

    categorical_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
        ('onehot', OneHotEncoder(handle_unknown='ignore'))])

    preprocessor = ColumnTransformer(transformers=[
        ('cat', categorical_transformer, selector(dtype_include=['object'])),
        ('num', numeric_transformer, selector(dtype_include=['float64','int64']))

    X = pd.DataFrame(preprocessor.fit_transform(X))
    return X 
  • After dropping rows, I defined the feature of matrix and the target, preprocessed and then split the data, like so:
# make feature matrix and target matrix
X = dfWithDroppedRows.drop(columns=['y'])
y = dfWithDroppedRows['y']

# encode target variable 
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)

# preprocess feature matrix

# Split data into training and testing data
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)
  • Finally, to calculate if the missing values are identically distributed (originally) in the two classes, I ran the following
np.count_nonzero(df.query("y == 'No'").isna()) / df.query("y == 'No'").size
np.count_nonzero(df.query("y == 'Yes'").isna()) / df.query("y == 'Yes'").size

So the majority class has about 28% missing data and the minority class has about 25% missing data.

  • 2
    $\begingroup$ How did you deal with the missing values originally? Did you remake the split after dropping rows? Are the missing values identically distributed (originally) in the two classes? $\endgroup$
    – Ben Reiniger
    Commented Jan 22, 2021 at 16:44
  • $\begingroup$ @BenReiniger I have responded to your questions in an edit to the post. $\endgroup$
    – sums22
    Commented Jan 22, 2021 at 18:01

1 Answer 1


You have a combination of two problems in your data:

  • imbalance
  • missing values

In your experiments there's a confusion about what the true distribution of the data is (or should be): either the "real data" is 97% no, or the "real data" is after removing missing values in which case it's almost balanced. It's very important to decide this based on the problem that you're trying to solve: "in production", does the model have to produce a prediction for every instance even if it has missing values? If yes the true distribution is 97% no (original problem). If no, then the model only predicts for "complete" instances, i.e. many instances are discarded due to missing values (and this happens much more often with "no").

This is a crucial point because whichever way you train the model, it should be evaluated on a test set which reflects the true distribution of the data.

I would assume that your real goal is to predict for every instance, i.e. you don't want to ignore instances even if they have missing values. I will try to address both options though:

  • option A: real data is 97% no.
  • option B: real data is 58% yes.
  1. Why did this method outperform other oversampling and undersampling techniques?

The two methods were evaluated on two very different test sets, so the performance between them is simply not comparable.

  • If the resampling experiments were properly evaluated on the original data (not resampled), then they provide you with a reliable estimate of the performance in option A.
  • If the resampling experiments were (wrongly) evaluated on resampled data, then the difference is certainly due to the missing values, because imputing a very large proportion of the data causes a lot of noise. In this case the resampling experiments are neither valid for option A (wrong distribution in the test set) nor B (missing values in the test set).
  1. Is it acceptable that what was previously a minority class is now the majority class or can this cause overfitting?

It depends which problem you're trying to solve:

  • For the original problem "option A", no it is not acceptable to modify the distribution in the test set.
  • For the new problem "option B", the majority class is "yes". The original data with 97% "no" is irrelevant.
  1. Is it realistic to build a model that relies on input with no missing data for the majority class samples?

This is about specifying the exact problem you want to solve, that's for you to decide :)

  • 1
    $\begingroup$ @sums22 it's a mistake to interpret these results as an improvement of performance, because the two are not evaluated on the same task: the first task with strong imbalance and lots of missing values is much harder than the second one, that's the main reason why the performance differs. If you want to know the exact reasons which explain why the first problem is "harder", you could run 2 additional experiments: one with missing values but balanced data, one with imbalance but no missing values. This way you could see how much each factor contributes to the difference. $\endgroup$
    – Erwan
    Commented Jan 25, 2021 at 11:36
  • 1
    $\begingroup$ Yes, exactly: the big difference is indeed whether changes are made only in the training set or the test set. the changes made in the training set are part of the method we use in order to reach the goal, whereas modifying the test set means changing the goal itself. I suspect that a big part of the problem is indeed the imputing, it's a valid method but personally I try to avoid it because we can't really know if the resulting data "makes sense", it's basically generating artificial data. $\endgroup$
    – Erwan
    Commented Jan 25, 2021 at 12:18
  • 2
    $\begingroup$ It's a complex issue: expert knowledge is important to understand what the missing values "mean" (if anything) and decide what to do. In general it's important to distinguish missing at random vs not at random, there have been a few questions about this. There are cases where the instances can be removed, or even the feature. Otherwise an option I tend to use is to represent the fact that the value is missing with either a special value if the ... $\endgroup$
    – Erwan
    Commented Jan 25, 2021 at 12:46
  • 1
    $\begingroup$ ... feature is categorical or an additional boolean feature if not (like in my answer in the last link). But I'm not particularly expert on this, there might be more solid approaches I'm not aware of. $\endgroup$
    – Erwan
    Commented Jan 25, 2021 at 12:47
  • 1
    $\begingroup$ @sums22 yes, that's what I meant: you would need to train and evaluate on a dataset which has the required property (either balanced with missing values or imbalanced without missing values). These experiments would have no other goal than understanding what happens, of course. You could use a subset of data for example, since the effect would probably be similar even if the performance is not optimal. $\endgroup$
    – Erwan
    Commented Jan 27, 2021 at 23:18

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.