Question 12 1. Train a Random Forests model where the number of estimators is 100 using the training dataset. 2. Make predictions using the test dataset 3. Compute accuracy and save as accuracy Hints: • You need to use the RandomForestClassifier function. Instantiate a RandomForestClassifier object and pass the number of estimators to the function. Train the model using the X_train and y_train. Then make predictions using X_test. Then compute the accuracy using the predicted values and y_test. • Check Module 6d: Model Performance and _Module 5c: Classification In [162]: # Your code to train random forest, make predictions, and compute accuracy goes in here accuracy = # compute accuracy here Do not alter the below cell. It is a test case for Question 6 In [163]: try: if (accuracy > 0.80): score['question 12'] = 'pass' else: score['question 12'] = 'fail' except: score['question 12'] = 'fail' score Out[163]: {'question 1': 'pass', 'question 2': 'pass', 'question 3': 'pass', 'question 4': 'pass', 'question 5': 'pass', 'question 6': 'pass', 'question 7': 'pass', 'question 8': 'pass', 'question 9': 'pass', 'question 10': 'pass', 'question 11': 'pass', 'question 12': 'pass'}
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ensemble and accuracy_score from sklearn.metrics. If you haven't split your dataset into training and testing sets, you'll also need to import train_test_split from sklearn.model_selection. ```python from sklearn.ensemble import RandomForestClassifier from Show more…
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