GBDT_solver.py 文件源码

def test():
    iris = load_iris()  
    #print iris
    #print iris['target'].shape  
    gbdt=GradientBoostingRegressor(n_estimators=1000, max_depth=4) 
    gbdt.fit(iris.data[:120],iris.target[:120])

    #Save GBDT Model
    joblib.dump(gbdt, 'GBDT.model') 

    predict = gbdt.predict(iris.data[:120])
    total_err = 0
    for i in range(len(predict)):
        print predict[i],iris.target[i]
        err = predict[i] - iris.target[i]
        total_err += err * err
    print 'Training Error: %f' % (total_err / len(predict))

    pred = gbdt.predict(iris.data[120:])
    error = 0
    for i in range(len(pred)):
        print pred[i],iris.target[i+120]
        err = pred[i] - iris.target[i+120]
        error += err * err
    print 'Test Error: %f' % (error / len(pred))