TypeError: estimator should be an estimator implementing 'fit' method
TypeError: estimator should be an estimator implementing 'fit' method
我从 Stepik 解决了这个问题：
One tree is good, but where are the guarantees that it is the best, or
at least close to it? One of the ways to find a more or less optimal
set of tree parameters is to iterate over a set of trees with
different parameters and choose the appropriate one. For this purpose,
there is a GridSearchCV class that iterates over each of the
combinations of parameters among those specified for the model, trains
it on the data and performs cross-validation. After that, the model
with the best parameters is stored in the .best_estimator_ attribute.
Now the task is to iterate over all the trees on the iris data
according to the following parameters: maximum depth - from 1 to 10
levels the minimum number of samples for separation is from 2 to 10
minimum number of samples per sheet - from 1 to 10 and store the best
tree in the variable best_tree. Name the variable with GridSearchCV
search.
Here is my solution:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
parameters = {'max_depth': range(1, 10), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 10)}
search = GridSearchCV(iris, parameters)
search.fit(X, y)
best_tree = search.estimator
为什么会出现此错误？：
Traceback (most recent call last):
File "jailed_code", line 22, in <module>
search.fit(X, y)
File "/home/stepic/instances/master-plugins/sandbox/python3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 595, in fit
self.estimator, scoring=self.scoring)
File "/home/stepic/instances/master-plugins/sandbox/python3/lib/python3.6/site-packages/sklearn/metrics/scorer.py", line 342, in _check_multimetric_scoring
scorers = {"score": check_scoring(estimator, scoring=scoring)}
File "/home/stepic/instances/master-plugins/sandbox/python3/lib/python3.6/site-packages/sklearn/metrics/scorer.py", line 274, in check_scoring
"'fit' method, %r was passed" % estimator)
TypeError: estimator should be an estimator implementing 'fit' method, {'data': array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
...
您尚未将任何估算器传递给您的 GridSearchCV 函数。您必须将要适合 GridSearCV 的估算器的实例传递给 GridSearCV，但是您只是传递不是估算器的 iris
您传递了数据集而不是估算器。如果你还没有，看看这个
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
这应该有效
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
parameters = {'max_depth': range(1, 10), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 10)}
search = GridSearchCV(estimator=DecisionTreeClassifier(),
param_grid=parameters)
search.fit(X, y)
search.cv_results_
我从 Stepik 解决了这个问题：
One tree is good, but where are the guarantees that it is the best, or at least close to it? One of the ways to find a more or less optimal set of tree parameters is to iterate over a set of trees with different parameters and choose the appropriate one. For this purpose, there is a GridSearchCV class that iterates over each of the combinations of parameters among those specified for the model, trains it on the data and performs cross-validation. After that, the model with the best parameters is stored in the .best_estimator_ attribute. Now the task is to iterate over all the trees on the iris data according to the following parameters: maximum depth - from 1 to 10 levels the minimum number of samples for separation is from 2 to 10 minimum number of samples per sheet - from 1 to 10 and store the best tree in the variable best_tree. Name the variable with GridSearchCV search. Here is my solution:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
parameters = {'max_depth': range(1, 10), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 10)}
search = GridSearchCV(iris, parameters)
search.fit(X, y)
best_tree = search.estimator
为什么会出现此错误？：
Traceback (most recent call last):
File "jailed_code", line 22, in <module>
search.fit(X, y)
File "/home/stepic/instances/master-plugins/sandbox/python3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 595, in fit
self.estimator, scoring=self.scoring)
File "/home/stepic/instances/master-plugins/sandbox/python3/lib/python3.6/site-packages/sklearn/metrics/scorer.py", line 342, in _check_multimetric_scoring
scorers = {"score": check_scoring(estimator, scoring=scoring)}
File "/home/stepic/instances/master-plugins/sandbox/python3/lib/python3.6/site-packages/sklearn/metrics/scorer.py", line 274, in check_scoring
"'fit' method, %r was passed" % estimator)
TypeError: estimator should be an estimator implementing 'fit' method, {'data': array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
...
您尚未将任何估算器传递给您的 GridSearchCV 函数。您必须将要适合 GridSearCV 的估算器的实例传递给 GridSearCV，但是您只是传递不是估算器的 iris
您传递了数据集而不是估算器。如果你还没有，看看这个 https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
这应该有效
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
parameters = {'max_depth': range(1, 10), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 10)}
search = GridSearchCV(estimator=DecisionTreeClassifier(),
param_grid=parameters)
search.fit(X, y)
search.cv_results_