20 lines
707 B
Python
20 lines
707 B
Python
import pandas as pd
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from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
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from math import sqrt
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# Load the predictions data
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data = pd.read_csv('beer_review_sentiment_predictions.csv')
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y_pred = data['Predictions']
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y_test = data['Actual']
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y_test_binary = (y_test >= 3).astype(int)
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# Calculate metrics
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accuracy = accuracy_score(y_test_binary, y_pred.round())
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precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred.round(), average='micro')
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rmse = sqrt(mean_squared_error(y_test, y_pred))
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print(f'Accuracy: {accuracy}')
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print(f'Micro-avg Precision: {precision}')
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print(f'Micro-avg Recall: {recall}')
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print(f'F1 Score: {f1}')
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print(f'RMSE: {rmse}') |