Model dasar

Memenangi Kompetisi Kaggle dengan Python

Yauhen Babakhin

Kaggle Grandmaster

Tahap pemodelan

komponen tahap pemodelan

Tahap pemodelan

model dasar pada tahap pemodelan

Validasi taksi New York City

# Read data
taxi_train = pd.read_csv('taxi_train.csv')
taxi_test = pd.read_csv('taxi_test.csv')

from sklearn.model_selection import train_test_split

# Create local validation
validation_train, validation_test = train_test_split(taxi_train,
                                                     test_size=0.3,
                                                     random_state=123)

Model dasar I

import numpy as np
# Assign the mean fare amount to all the test observations
taxi_test['fare_amount'] = np.mean(taxi_train.fare_amount)

# Write predictions to the file
taxi_test[['id','fare_amount']].to_csv('mean_sub.csv', index=False)

RMSE validasi	RMSE LB publik	Posisi LB publik
9.986	9.409	1449 / 1500

Model dasar II

# Calculate the mean fare amount by group
naive_prediction_groups = taxi_train.groupby('passenger_count').fare_amount.mean()

# Make predictions on the test set
taxi_test['fare_amount'] = taxi_test.passenger_count.map(naive_prediction_groups)

# Write predictions to the file
taxi_test[['id','fare_amount']].to_csv('mean_group_sub.csv', index=False)

RMSE validasi	RMSE LB publik	Posisi LB publik
9.978	9.407	1411 / 1500

Model dasar III

# Select only numeric features
features = ['pickup_longitude', 'pickup_latitude',
            'dropoff_longitude', 'dropoff_latitude', 'passenger_count']

from sklearn.ensemble import GradientBoostingRegressor

# Train a Gradient Boosting model
gb = GradientBoostingRegressor()
gb.fit(taxi_train[features], taxi_train.fare_amount)

# Make predictions on the test data
taxi_test['fare_amount'] = gb.predict(taxi_test[features])

Model dasar III

# Write predictions to the file
taxi_test[['id','fare_amount']].to_csv('gb_sub.csv', index=False)

RMSE validasi	RMSE LB publik	Posisi LB publik
5.996	4.595	1109 / 1500

Hasil sementara

Model	RMSE validasi	RMSE LB publik
Rata-rata sederhana	9.986	9.409
Rata-rata per grup	9.978	9.407
Gradient Boosting	5.996	4.595

Korelasi dengan Public Leaderboard

Model	RMSE validasi	RMSE LB publik
Model A	3.500	3.800
Model B	3.300	4.100
Model C	3.200	3.900

Model	RMSE validasi	RMSE LB publik
Model A	3.400	3.900
Model B	3.100	3.400
Model C	2.900	3.300

Ayo berlatih!

Memenangi Kompetisi Kaggle dengan Python