Random Forest Classification on Target encoded data
- The code fits the RandomForestClassifier (rf_classifier) to the training data (X_train_te, y_train) using the fit() method.
- Predictions are made on the target encoded testing feature set (X_test_te) using the predict() method, resulting in predicted target values (y_pred_te).
- The accuracy of the model is calculated by comparing the predicted target values (y_pred_te) with the actual target values from the testing set (y_test) using the accuracy_score() function.
rf_classifier.fit(X_train_te, y_train)
y_pred_te = rf_classifier.predict(X_test_te)
# Calculate accuracy
accuracy_te = accuracy_score(y_test, y_pred_te)
print("Target Encoder Accuracy: ", accuracy_te)
Output:
Target Encoder Accuracy: 0.9739884393063584
In conclusion, the choice of encoding technique for categorical variables in random forest classification significantly influences model performance. Ordinal Encoding preserves ordinal relationships, One-Hot Encoding handles unordered categories effectively, and Target Encoding captures predictive information. Understanding these techniques empowers data scientists to preprocess categorical data effectively, enhancing model accuracy and interpretability.
How to fit categorical data types for random forest classification?
Categorical variables are an essential component of many datasets, representing qualitative characteristics rather than numerical values. While random forest classification is a powerful machine-learning technique, it typically requires numerical input data. Therefore, encoding categorical variables into a suitable format is a crucial step in preparing data for random forest classification. In this article, we’ll explore different encoding methods and their applications in fitting categorical data types for random forest classification.
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