28 lines
845 B
Python
28 lines
845 B
Python
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import json
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import numpy as np
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import classification_report
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from sklearn.model_selection import train_test_split
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def eval():
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y_pred_full, y_test_full = [], []
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# Re-train 10 times in order to reduce effects of randomness
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for i in range(10):
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### TODO: Exercise 5
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### 1. Load data from traces file
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### 2. Split data into X_train, X_test, y_train, y_test with train_test_split
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### 3. Train classifier with X_train and y_train
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### 4. Use classifier to make predictions on X_test. Save the result to a variable called y_pred
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# Do not modify the next two lines
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y_test_full.extend(y_test)
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y_pred_full.extend(y_pred)
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### TODO: Exercise 5 (continued)
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### 5. Print classification report using y_test_full and y_pred_full
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if __name__ == "__main__":
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eval()
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