मॉडल ट्यून क्यों करें?

XGBoost के साथ Extreme Gradient Boosting

Sergey Fogelson

Head of Data Science, TelevisaUnivision

अनट्यूनड मॉडल उदाहरण

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

ट्यूनड मॉडल उदाहरण

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

चलो कुछ मॉडल ट्यून करें!

XGBoost के साथ Extreme Gradient Boosting