Kaggle competition: Lyft Motion Prediction for Autonomous Vehicles
<SOS> metric: PostProcessorKernel Kaggle data source: lyft-motion-prediction-autonomous-vehicles<choose_model_class>
import numpy as np
import pandas as pd
GBoost = GradientBoostingRegressor(n_estimators=600, learning_rate=0.05,
max_depth=4, max_features='auto',
min_samples_leaf=15, min_samples_split=5,
loss='huber', random_state =5 )<train_model>
sub = pd.read_csv('.. /input/lyft-prediction-files/submission.csv')
 sub
class Average_models(BaseEstimator,RegressorMixin,TransformerMixin):
def __init__(self,models):
self.models=models
def fit(self,X,y):
self.models_=[clone(x)for x in self.models]
for model in self.models_:
model.fit(X,y)
 return self
def predict(self,X):
predictions= np.column_stack([model.predict(X)for model in self.models_])
 return np.mean(predictions,axis=1 )<find_best_model_class>
sub.to_csv('submission.csv', index=False )
<save_to_csv><EOS> 
sub.to_csv('submission.csv', index=False )