Introduction: Dealing with Imbalanced Data in Decision Trees

Imbalanced datasets present a significant challenge in machine learning, particularly in the realm of decision tree classifiers. This situation often arises when the number of samples in one class is significantly higher than in the other, leading to models that tend to favor the majority class. In this article, we will explore various techniques to address this issue, including choosing proper evaluation metrics, resampling methods, SMOTE, BalancedBaggingClassifier, and threshold moving.

1. Choosing Proper Evaluation Metrics

Traditional accuracy metrics can be misleading in imbalanced datasets. Instead, F1 Score, which is the harmonic mean of precision and recall, is a more appropriate metric. Precision measures how accurate the classifier’s prediction of a specific class is, while recall measures the classifier’s ability to identify a class. The F1 score improves if both the number and quality of predictions are improved, keeping a balance between precision and recall.

Accuracy alone may not provide a clear picture of the model's performance. For instance, if a classifier predicts the minority class incorrectly, it will boost the precision but lower the F1 score, indicating an imbalance. Similarly, if the classifier identifies the minority class poorly, it will increase the false negatives and lower the recall and F1 score.

2. Resampling Oversampling and Undersampling

Resampling techniques are commonly used to adjust the class distribution of imbalanced datasets. There are two main approaches:

Oversampling: This technique involves increasing the number of instances in the minority class to match those in the majority class. This can be done through replacement (randomly adding more instances), which results in an overreamped dataset.Undersampling: Conversely, this involves reducing the number of instances in the majority class to match those in the minority class, which can be done randomly or via undersampling specific instances.

Welcome to a code snippet demonstrating how to use the Resample method from the sklearn.utils library in Python:

from sklearn.utils import resampledf_majority  df_train[df_train['Is_Lead']0]df_minority  df_train[df_train['Is_Lead']1]df_minority_upsampled  resample(df_minority,                                   replaceTrue,                                     n_samples131177,                                   random_state42)df_upsampled  ([df_minority_upsampled, df_majority])

The F1 score of the classifier will only improve if the classifier correctly identifies more instances of the minority class. By achieving class balance, the classifier can focus on both classes equally.

3. SMOTE: Synthetic Minority Oversampling Technique

SMOTE (Synthetic Minority Oversampling Technique) is a more sophisticated oversampling method that synthesizes new instances from the existing minority class data rather than simply repeating the samples. This approach helps in avoiding the addition of duplicate records, which can lead to overfitting. SMOTE works by selecting a random nearest neighbor within the minority class and creating a synthetic instance in the feature space. Here's a code example:

from imblearn.over_sampling import SMOTEsm  SMOTE(sampling_strategy'minority', random_state42)X_resampled, y_resampled  _resample(df_train.drop('Is_Lead', axis1), df_train['Is_Lead'])

By using SMOTE, the minority class becomes more representative, improving the model's performance without biasing the classifier towards the majority class. This technique is particularly useful in datasets where the minority class is sparse.

4. BalancedBaggingClassifier

BalancedBaggingClassifier is a modification of the traditional BaggingClassifier designed to handle imbalanced datasets. It randomly under-samples the majority class and over-samples the minority class before training. This classifier takes parameters like sampling_strategy and replacement to control the resampling process:

from imblearn.ensemble import BalancedBaggingClassifierfrom  import DecisionTreeClassifierclassifier  BalancedBaggingClassifier(base_estimatorDecisionTreeClassifier(),                                       sampling_strategy'not majority',                                       replacementFalse,                                       random_state42)(X_train, y_train)predictions  (X_test)

This method ensures that each class is equally represented during the training process, thereby reducing the bias towards the majority class and improving overall performance.

5. Threshold Moving

In cases where classifiers provide probability estimates, a simple threshold (commonly 0.5) might not be optimal for separating classes, especially in imbalanced datasets. The optimal threshold can be determined using various methods, such as ROC Curves or Precision-Recall Curves. Another approach includes grid searching through a range of thresholds:

from sklearn.ensemble import RandomForestClassifierrf_model  RandomForestClassifier()rf_(X_train, y_train)predicted_proba  rf__proba(X_test)output  [[0.97, 0.03], [0.94, 0.06], [0.78, 0.22], ...]step_factor  0.05threshold_value  0.2roc_score  0predicted  (predicted_proba[:, 1]  threshold_value).astype(int)roc_score  roc_auc_score(y_test, predicted)if roc_score  roc_score:    roc_score  roc_auc_score(y_test, predicted)    thrsh_score  threshold_valuethreshold_value   step_factorprint("Optimum Threshold:", thrsh_score, "ROC:", roc_score)

The goal is to find the threshold that maximizes the classifier's performance, tailoring the decision boundary specifically for the minority class. This customized threshold helps in optimizing the separation between classes.