Jupiter notebook, не получается человеческое дерево решений и случайный лес

from sklearn.tree import DecisionTreeClassifier
import numpy as np
import pandas as pd
np.random.seed (42)
dataset =pd.read_csv(r'C:\Users\VADIMETS\Desktop\archive\creditcard_2023.csv')
X =dataset.iloc[:,3:5].values
y=dataset.iloc[:,0].values
from  sklearn.model_selection import  train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.fit_transform(X_test)
dtree_= DecisionTreeClassifier (criterion='gini', max_depth=4,random_state=1)
dtree_.fit(X_train_std, y_train)
from matplotlib import pyplot as plt
 
markers = ('s','x','o','^', 'v')
colors=('red', 'blue', 'gray', 'cyan')
plt.style.use('ggplot')
plt.rcParams ['figure.figsize']= 10, 6
%matplotlib inline
 
X_range = np.linspace(X_test_std.min(), X_test_std.max(),100)
xx1, xx2 = np.meshgrid(X_range, X_range)
y_hat = dtree_.predict(np.c_[xx1.ravel(), xx2.ravel()])
y_hat = y_hat.reshape(xx1.shape)
plt.contourf(xx1, xx2, y_hat, alpha=0.9)
for l, c, m in zip (np.unique(y_train), colors, markers): plt.scatter(X_train_std [y_train ==1, 0], X_train_std[y_train==1, 1], c=c, label=l , marker=m)
plt.title("Дерево решений")
plt.show()
from sklearn  import tree
fig=plt.figure(figsize=(15,7))
tree.plot_tree(dtree_, fontsize=7)
plt.show()
from sklearn.ensemble import RandomForestClassifier
rf=RandomForestClassifier(n_estimators=300,random_state=42)
rf.fit(X_train_std,y_train)
from matplotlib import pyplot as plt
markers = ('s','x','o','^', 'v')
colors=('red', 'blue', 'gray', 'cyan')
plt.style.use('ggplot')
plt.rcParams ['figure.figsize']= 10, 6
%matplotlib inline
 
X_range = np.linspace(X_test_std.min(), X_test_std.max(),100)
xx1, xx2 = np.meshgrid(X_range, X_range)
y_hat = dtree_.predict(np.c_[xx1.ravel(), xx2.ravel()])
y_hat = y_hat.reshape(xx1.shape)
plt.contourf(xx1, xx2, y_hat, alpha=0.2)
for l, c, m in zip (np.unique(y_train), colors, markers): plt.scatter(X_train_std [y_train ==1, 0], X_train_std[y_train==1, 1], c=c, label=l , marker=m)
plt.title("Случайный лес")
plt.show()
from sklearn.metrics import accuracy_score
tree_ = dtree_.fit (X_train,y_train)
y_train_pred = tree_.predict (X_train)
y_test_pred = tree_.predict (X_test)
 
tree_train = accuracy_score(y_train,y_train_pred)
tree_test = accuracy_score(y_test, y_test_pred)
print('Меры правильности принятия решений при обучении/испытании %.f/%.3f' % (tree_train, tree_test))