Perché ottimizzare il modello?

Extreme Gradient Boosting con XGBoost

Sergey Fogelson

Head of Data Science, TelevisaUnivision

Esempio di modello non ottimizzato

import pandas as pd
import xgboost as xgb
import numpy as np
housing_data = pd.read_csv("ames_housing_trimmed_processed.csv")
X,y = housing_data[housing_data.columns.tolist()[:-1]],
        housing_data[housing_data.columns.tolist()[-1]]
housing_dmatrix = xgb.DMatrix(data=X,label=y)

untuned_params={"objective":"reg:squarederror"}

untuned_cv_results_rmse = xgb.cv(dtrain=housing_dmatrix,
        params=untuned_params,nfold=4,
        metrics="rmse",as_pandas=True,seed=123)

print("Untuned rmse: %f" %((untuned_cv_results_rmse["test-rmse-mean"]).tail(1)))

Untuned rmse: 34624.229980

Esempio di modello ottimizzato

import pandas as pd
import xgboost as xgb
import numpy as np
housing_data = pd.read_csv("ames_housing_trimmed_processed.csv")
X,y = housing_data[housing_data.columns.tolist()[:-1]],
     housing_data[housing_data.columns.tolist()[-1]]
housing_dmatrix = xgb.DMatrix(data=X,label=y)

tuned_params = {"objective":"reg:squarederror",'colsample_bytree': 0.3,
    'learning_rate': 0.1, 'max_depth': 5}

tuned_cv_results_rmse = xgb.cv(dtrain=housing_dmatrix,
     params=tuned_params, nfold=4, num_boost_round=200, metrics="rmse",
     as_pandas=True, seed=123)

print("Tuned rmse: %f" %((tuned_cv_results_rmse["test-rmse-mean"]).tail(1)))

Tuned rmse: 29812.683594

Ottimizziamo qualche modello!

Extreme Gradient Boosting con XGBoost