Issue
I am on my second day of re-taking Python for the gazillionth time! I am doing a tutorial on ML in Python, using the following code:
import sklearn.tree
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import tree
music_data = pd.read_csv('music.csv')
x = music_data.drop(columns=['genre'])
y = music_data['genre']
model = DecisionTreeClassifier()
model.fit(x,y)
tree.export_graphviz(model, out_file='music-recommender.dot',
feature_names=['age','gender'],
class_names= sorted(y.unique()),
label='all',
rounded=True,
filled=True)
I keep getting the following error:
ImportError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_13088/3820271611.py in <module>
2 import pandas as pd
3 from sklearn.tree import DecisionTreeClassifier
----> 4 from sklearn.tree import tree
5
6 music_data = pd.read_csv('music.csv')
ImportError: cannot import name 'tree' from 'sklearn.tree' (C:\Anaconda\lib\site-packages\sklearn\tree\__init__.py)
I've tried to find a solution online, but I don't think it's the version of Python/Anaconda because I literally just installed both. I also don't think it's the sklearn.tree since I was able to import DecisionClassifer.
Solution
As this answer indicates, you're looking at some older code; this is always a risk with programming. But there's another thing you need to know about your code.
First off, scikit-learn
contains several modules, and almost everything you need from it is in one of those. In my experience, most people import things like this:
from sklearn.tree import DecisionTreeRegressor # A regressor class.
from sklearn.tree import plot_tree # A helpful function.
from sklearn.metrics import mean_squared_error # An evaluation function.
It looks like the tutorial wants something similar to plot_tree()
. This new-ish function is much easier to use than the older Graphviz visualization. So unless you really need the DOT file for some reasons, you should be able to do this:
from sklearn.tree import plot_tree
sklearn.tree.plot_tree(model)
Bottom line: there will probably be more broken things in that material. So if I were you I'd either make a new environment with a version of sklearn
matching whatever material you're using... or ditch that material and look for something newer.
Answered By - kwinkunks
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