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import pandas as pd from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score # Assuming you have a DataFrame 'df' with true labels 'y_true' and predicted labels 'y_pred' y_true = df['true_labels'] y_pred = df['predicted_labels'] # Calculate accuracy accuracy = accuracy_score(y_true, y_pred) # Calculate precision precision = precision_score(y_true, y_pred, average='weighted') # Calculate recall (sensitivity) recall = recall_score(y_true, y_pred, average='weighted') # Calculate F1-score (harmonic mean of precision and recall) f1score = f1_score(y_true, y_pred, average='weighted') print(f"Accuracy: {accuracy:.4f}") print(f"Precision: {precision:.4f}") print(f"Recall: {recall:.4f}") print(f"F1 Score: {f1score:.4f}")
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