如何解决如何创建机器学习模型列表?
我正在针对30个不同的数据集(具有相同维度)训练一系列机器学习模型。我想将这些模型存储在列表中。所有模型都是相同的,即DecisionTreeRegressor(),但每个模型都在不同的数据集上训练。
model_list = []
for i in range(30):
model = DecisionTreeRegressor(max_depth= None,criterion ='mse')
model.fit(x[i],y[i])
model_list.append(model)
我可以使用上面的代码,但是它将为每个模型(即“模型”)以相同的名称存储所有模型。我想用不同的名称存储每个名称,例如['model_1','model_2','model_3'等。 ]
以下是数据x和y的尺寸。供你参考。请帮忙。
x.shape = 30
,y.shape = 30
x[0].shape = (500,5) and y[0].shape = (500)
解决方法
test_model = 'x[0].shape = (500,5) and y[0].shape = (500)'
您可以按照以下步骤进行操作,也可以在for
循环中添加名称
model_df = pd.DataFrame()
for i in range(30):
model_df = model_df.append([test_model])
model_df=model_df.reset_index(drop=True)
model_df['model_name'] = ['model no: ']+model_df.index.astype(str)
print(model_df)
0 model_name
0 x[0].shape = (500,5) and y[0].shape = (500) model no: 0
1 x[0].shape = (500,5) and y[0].shape = (500) model no: 1
2 x[0].shape = (500,5) and y[0].shape = (500) model no: 2
3 x[0].shape = (500,5) and y[0].shape = (500) model no: 3
4 x[0].shape = (500,5) and y[0].shape = (500) model no: 4
5 x[0].shape = (500,5) and y[0].shape = (500) model no: 5
6 x[0].shape = (500,5) and y[0].shape = (500) model no: 6
7 x[0].shape = (500,5) and y[0].shape = (500) model no: 7
8 x[0].shape = (500,5) and y[0].shape = (500) model no: 8
9 x[0].shape = (500,5) and y[0].shape = (500) model no: 9
10 x[0].shape = (500,5) and y[0].shape = (500) model no: 10
11 x[0].shape = (500,5) and y[0].shape = (500) model no: 11
12 x[0].shape = (500,5) and y[0].shape = (500) model no: 12
13 x[0].shape = (500,5) and y[0].shape = (500) model no: 13
14 x[0].shape = (500,5) and y[0].shape = (500) model no: 14
15 x[0].shape = (500,5) and y[0].shape = (500) model no: 15
16 x[0].shape = (500,5) and y[0].shape = (500) model no: 16
17 x[0].shape = (500,5) and y[0].shape = (500) model no: 17
18 x[0].shape = (500,5) and y[0].shape = (500) model no: 18
19 x[0].shape = (500,5) and y[0].shape = (500) model no: 19
20 x[0].shape = (500,5) and y[0].shape = (500) model no: 20
21 x[0].shape = (500,5) and y[0].shape = (500) model no: 21
22 x[0].shape = (500,5) and y[0].shape = (500) model no: 22
23 x[0].shape = (500,5) and y[0].shape = (500) model no: 23
24 x[0].shape = (500,5) and y[0].shape = (500) model no: 24
25 x[0].shape = (500,5) and y[0].shape = (500) model no: 25
26 x[0].shape = (500,5) and y[0].shape = (500) model no: 26
27 x[0].shape = (500,5) and y[0].shape = (500) model no: 27
28 x[0].shape = (500,5) and y[0].shape = (500) model no: 28
29 x[0].shape = (500,5) and y[0].shape = (500) model no: 29
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