Random Forest Classification on One-hot encoded data
- The code fits the RandomForestClassifier (rf_classifier) to the training data (X_train_oh, y_train) using the fit() method.
- Predictions are made on the one-hot encoded testing feature set (X_test_oh) using the predict() method, resulting in predicted target values (y_pred_oh).
- The accuracy of the model is calculated by comparing the predicted target values (y_pred_oh) with the actual target values from the testing set (y_test) using the accuracy_score() function.
rf_classifier.fit(X_train_oh, y_train)
y_pred_oh = rf_classifier.predict(X_test_oh)
# Calculate accuracy
accuracy_oh = accuracy_score(y_test, y_pred_oh)
print("One-Hot Encoder Accuracy: ", accuracy_oh)
Output:
One-Hot Encoder Accuracy: 0.9595375722543352
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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