sklearn.exceptions.NotFittedError: This DecisionTreeClassifier instance is not fitted yet

Question

im try to visualize decision tree with using mode from image data in python without graphviz using DecisionTreeClassifier but im keep getting error

sklearn.exceptions.NotFittedError: This DecisionTreeClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

even i try in google colab and VScode it still getting error. My dataset only have 2 columns is ModusH and Index.

Here my dataset examples Dataset

And Here the Code

datapisang= pd.read_csv('DataModusdiperbaiki.csv')           
X= datapisang[['ModusH']]                                    
Y= datapisang[['Index']]                                     
X_train, X_test, Y_train, Y_test = train_test_split(X, Y)    
# Model
DT_model= DecisionTreeClassifier()                            
DT_model.fit(X_train,Y_train)                               
DT_model.print_tree()                                       
data = [Modus_citra] # Mode Image                                        
hasilprediksi = DT_model.predict([data])                     

fn = ['ModusH'] 
cn = ['Index'] 

fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)

tree.plot_tree(DT_model,
           feature_names = fn, 
           class_names=cn,
           filled = True);

fig.savefig('imagename.png')

Im try to get the visualize but it keep error everytime even using graphviz. Im new to this topic, can someone help me ? i appreciate every help.

Answer 1

You should do it as below, as shown in documentation

from sklearn.datasets import load_iris
from sklearn import tree

clf = tree.DecisionTreeClassifier(random_state=0)
iris = load_iris()

clf = clf.fit(iris.data, iris.target)

tree.plot_tree(clf)

Based on edit:

I tried to regenerate your situation by creating a new dataframe; here is the solution:

df = pd.DataFrame(np.random.randint(0,100,size=(100, 1)), columns=["ModusH"])
df['Index'] = np.random.choice(  a=[0, 1, 2,3,4],size=df.shape[0])   

clf = tree.DecisionTreeClassifier(random_state=0)

clf = clf.fit(df.ModusH.to_numpy().reshape(-1, 1), df.Index)

tree.plot_tree(clf)