Baseline based on the gradient boosting
Let's build a final baseline based on the Random Forest. You've seen a huge score improvement moving from the grouping baseline to the Gradient Boosting in the video. Now, you will use sklearn's Random Forest to further improve this score.
The goal of this exercise is to take numeric features and train a Random Forest model without any tuning. After that, you could make test predictions and validate the result on the Public Leaderboard. Note that you've already got an "hour" feature which could also be used as an input to the model.
Este exercício faz parte do curso
Winning a Kaggle Competition in Python
Instruções do exercício
- Add the
"hour"feature to the list of numeric features. - Fit the
RandomForestRegressoron the train data with numeric features and"fare_amount"as a target. - Use the trained Random Forest model to make predictions on the test data.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
from sklearn.ensemble import RandomForestRegressor
# Select only numeric features
features = ['pickup_longitude', 'pickup_latitude', 'dropoff_longitude',
'dropoff_latitude', 'passenger_count', ____]
# Train a Random Forest model
rf = RandomForestRegressor()
rf.____(train[____], train.fare_amount)
# Make predictions on the test data
test['fare_amount'] = ____.____(test[features])
# Write predictions
test[['id','fare_amount']].to_csv('rf_sub.csv', index=False)