在lightGBM模型中,有两个与套袋相关的参数。
bagging_fraction
bagging_freq (frequency for bagging
0 means disable bagging; k means perform bagging at every k
iteration
Note: to enable bagging, bagging_fraction should be set to
value smaller than 1.0 as well)
我可
def bagging_and_trees_growth(samples, network, tree_num):
trees = []
for i in range(tree_num):
bootstrap_samples = bagging(samples)
a_tree = tree_growth(network, bootstrap_samples)
trees.append(a_tree)
return trees
def agiled_random_forest(samp
我正在尝试在Kaggle Iowa住房数据集上训练一个LightGBM模型,我写了一个小脚本,在给定的范围内随机尝试不同的参数。我不确定我的代码出了什么问题,但是脚本用不同的参数返回相同的分数,这是不应该发生的。我在Catboost上尝试了相同的脚本,它可以正常工作,所以我猜问题出在LGBM上。 代码: import numpy as np
import pandas as pd
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selec
我正在测试一个简单的模型(knn),并尝试将结果与Ensamble进行比较。
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.datasets import load_iris
data = load_iris()
y = data.target
X = data.data
knn = KNeighborsClassif
我找到了定义:
Bagging is to use the same training for every predictor, but to train them on
different random subsets of the training set.
When sampling is performed with replacement, this method is
called bagging (short for bootstrap aggregating).
When sampling is performed without replacement, it is
我需要对LSTM使用打包方法,对时间序列数据进行训练。我已经定义了模型库,并使用KerasRegressor链接到scikit learn。但是有损失:'KerasRegressor‘对象没有’AttributeError‘属性。我怎么才能修复它呢? 更新:我使用了Manoj Mohan的方法(在第一条评论中),并成功地完成了fit步骤。但是,当我将Manoj Mohan的类修改为TypeError时,问题就来了。 class MyKerasRegressor(KerasRegressor):
def fit(self, x, y, **kwargs):
x
如何更改此代码以处理多行更新。@id是int,order_id是主键,但我想检查每一行的状态_b=‘袋’
select @id=inserted.order_id from inserted;
if update(status_b)
begin
if (select status_b from inserted)='bagged'
begin
if (select o.id
from [order] o
left join [print] p on o.id=p.order
我正在尝试将支持向量机与Scikit-learn集成,特别是优化超参数。我非常随机地得到以下错误:
File "C:\Users\jakub\anaconda3\envs\SVM_ensembles\lib\site-packages\sklearn\svm\_base.py", line 250, in _dense_fit
self.probB_, self.fit_status_ = libsvm.fit(
File "sklearn\svm\_libsvm.pyx", line 191, in sklearn.svm._libsvm.fi
我试图保存我的模型,以便它可以在ASP.NET程序中使用,我认为ONNX是一个很好的方法。问题是,即使在检查了文档并对其进行了一整天的搜索之后,我仍然得到相同的错误raise ValueError('Initial types are required. See usage of ' ValueError: Initial types are required. See usage of convert(...) in skl2onnx.convert for details。我不知道发生了什么,任何帮助都是非常感谢的!
我的代码
import onnxmltools
from
我在一个“葡萄酒”数据集上使用了决策树算法,这个数据集在1到10的范围内预测葡萄酒的质量,其中1是最差的,10是最好的,示例代码如下
import pandas as pd
data=pd.read_csv("wine.csv")
x=data.drop(columns="quality")# x has all the feature columns
y=data.quality# y has the label column
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,ra