python-scikit-learnXgboost regression usage example
from sklearn import datasets, model_selection
import xgboost as xgb
X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = xgb.XGBRegressor()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + cfrom sklearn importimport module from scikit-learn | import xgboost as xgbLoads XGBoost module |
load_diabetesloads sample diabetes database | model_selection.train_test_splitsplits given |
xgb.XGBRegressor(creates XGBoost regression model | .fit(train transformation model |
.predict(predict target variable based on given features dataset | |
Usage example
from sklearn import datasets, model_selection
import xgboost as xgb
X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = xgb.XGBRegressor()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))output
0.35751336429972946
More of Python Scikit Learn
- Using OneClassSVM for outliers detection example
- Using Ridge regression model example
- How to load wine dataset
- Use imputer for one column only
- How to get best parameters from grid search CV
- Grid search CV example
- Using quantile regression
- Using dbscan clustering example
- Neural network example
- Feature selection using KBest and chi2
See more codes...