treeimportDecisionTreeRegressorfromsklearn.linear_modelimportLinearRegression,ElasticNetfromsklearn.ensembleimportRandomForestRegressorfrom
sklearn.neighbors.KNeighborsRegressor with xgboost to use xgboost’s gradient boosted decision trees? *use sklearn.tree.DecisionTreeRegressor with xgboost to use xgboost’s gradient boosted decision trees? Thank you Anthony of Sydney Jason Brownlee March 19, 2021 at 6:16 am # No, as far as...
最后,这个例子展示了如何在回归任务中使用 LCE。 from lce import LCERegressor from sklearn.datasets import load_diabetes from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split # Load data and generate a train/test split data = load_diabetes() X_train,...
random_state=0) # Train LCERegressor with default parameters reg = LCERegressor(n_jobs=-1, random_state=0) reg.fit(X_train, y_train) # Make prediction y_pred = reg.predict(X_test) mse = mean_squared_error(y_test, reg.predict(X_test)) print("The mean squared error (MSE) on tes...
data=load_diabetes()X_train,X_test,y_train,y_test=train_test_split(data.data,data.target,random_state=0)# Train LCERegressorwithdefaultparameters reg=LCERegressor(n_jobs=-1,random_state=0)reg.fit(X_train,y_train)# Make prediction
# Train LCERegressor with default parametersreg = LCERegressor(n_jobs=-1, random_state=0)reg.fit(X_train, y_train)# Make prediction y_pred = reg.predict(X_test)mse = mean_squared_error(y_test, reg.predict(X_test))print("The mean squared error (MSE) on test set: {:.0f}"....
I am trying to implement an example using Java JDK8. The example says that when using the GPU, you must set the value of featuresCols using the setFeaturesCols() method. val xgbRegressor = new XGBoostRegressor(xgbParamFinal) .setLabelCol...
# Train LCERegressor with default parameters reg = LCERegressor(n_jobs=-1, random_state=0) reg.fit(X_train, y_train) # Make prediction y_pred = reg.predict(X_test) mse = mean_squared_error(y_test, reg.predict(X_test)) print("The mean squared error (MSE) on test set: {:.0f...
# Train LCERegressor with default parameters reg = LCERegressor(n_jobs=-1, random_state=0) reg.fit(X_train, y_train) # Make prediction y_pred = reg.predict(X_test) mse = mean_squared_error(y_test, reg.predict(X_test)) print("The mean squared error (MSE) on test set: {:.0f...
类class xgboost.XGBRFRegressor(learning_rate=1, subsample=0.8, colsample_bynode=0.8, reg_lambda=1e-05, **kwargs) Bases: xgboost.sklearn.XGBRegressor scikit-learn API for XGBoost random forest regression. 和上面的类:class xgboost.XGBRegressor(objective='reg:squarederror', **kwargs)参数和方法基...