In the field of machine learning and data science, imbalance data problem is quite common. It refers to the situation where the number of observations belonging to one class substantially outweighs the number of observations belonging to the other classes. This is often a challenging scenario for classification problems. There, it leads to a biased model, underperforming on the minority class.
Let's consider a cancer prediction model. The number of healthy cases is much higher than cancer cases, leading to an imbalance. If we feed this data to our model without any preprocessing steps, the model will likely be accurate with healthy cases but may fail to accurately predict cancer cases due to the limited data on the latter.
This bias towards majority class leads to a severe impact on the model's accuracy.
#Suppose we have a dataset "data" with two classes 0 and 1 (with 0 as the majority), and we are using a logistic regression model from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score model = LogisticRegression() X = data.drop('class',axis=1) #Features y = data['class'] #Target model.fit(X, y) predicted_classes = model.predict(X) accuracy = accuracy_score(y.values.flatten(),predicted_classes) print("Model Accuracy: ", accuracy)
Let's say; it gives us an accuracy of 90%. This might seem like a good performing model, but if we dive deeper, we may see that most of the predictions are for the majority class (i.e., class 0).
SMOTE or Synthetic Minority Over-sampling Technique is a commonly used approach to handle imbalanced datasets. It works by creating synthetic samples of the minority class.
from imblearn.over_sampling import SMOTE smote = SMOTE() X_smote, y_smote = smote.fit_resample(X, y) model.fit(X_smote, y_smote) predicted_classes_smote = model.predict(X_smote) accuracy_smote = accuracy_score(y_smote.values.flatten(),predicted_classes_smote) print("Model Accuracy after SMOTE: ", accuracy_smote)
After applying SMOTE before training the model, check the accuracy again. This time, the model's accuracy could hypothetically be reduced to 85%, but it's a more generalized model, providing better predictions for both classes.
In conclusion, it's crucial to be aware of the issue of imbalanced classes when dealing with a dataset for a classification problem. Approaches like SMOTE help to balance the data and provide a more generalized model, increasing the ultimate performance.