title: Decision Tree - Edges / Branches so Light that are invisible
body: <p>I am using the classic titanic dataset to build a decision tree. However, I am not sure what goes wrong with the edges or branches that are almost invisible.</p>
  
  <p>Here is the code for building the decision tree</p>
  
  <pre><code>    # Plant a new pruned tree 
  
      ideal_dt = DecisionTreeClassifier(random_state=6, ccp_alpha=optimal_alpha)
      ideal_dt = ideal_dt.fit(X_train, y_train)
  
      # Plot the confusion matrix
      plot_confusion_matrix(ideal_dt,X_test,y_test,display_labels=['Not Survived','Survived'])
      plt.grid(False);
  
      # Plot the tree
      plt.figure(figsize=(200,180))
      plot_tree(ideal_dt,filled=True,rounded=True, fontsize=120, class_names=labels,feature_names=data_features.columns);
  
      print('\nIdeal Decision Tree')
      # Training Score
      print('Training Set Accuracy:',ideal_dt.score(X_train,y_train))
      # Testing Score
      print('Testing Set Accuracy:',ideal_dt.score(X_test,y_test))
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/amti7.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/amti7.png" alt="enter image description here"></a></p>
  
  <p>Here is the setup:</p>
  
  <pre><code># Basic Import
  import pandas as pd
  import numpy as np
  import seaborn as sns
  import random
  import matplotlib.pyplot as plt
  
  # Hypothesis Testing
  from scipy.stats import ttest_ind, ttest_rel, ttest_1samp
  
  
  # Machine Learning Import
  import sklearn as skl
  from sklearn import datasets
  
  # Preprocessing
  from sklearn.preprocessing import LabelEncoder
  
  from sklearn.model_selection import train_test_split, cross_val_score
  
  # Linear Regression 
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import Ridge
  
  # KNN Classification
  from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import scale
  from sklearn.metrics import confusion_matrix
  from sklearn.metrics import plot_confusion_matrix
  from sklearn.metrics import f1_score
  from sklearn.decomposition import PCA
  from sklearn.model_selection import GridSearchCV
  
  # K-means clustering
  from sklearn.cluster import KMeans
  
  # Logistic Regression
  from sklearn.linear_model import LogisticRegression 
  
  # Decision Tree
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.tree import plot_tree
  from sklearn.model_selection import cross_val_score
  
  # Database Import
  import sqlite3
  from sqlite3 import Error
  
  # Measure Performance
  from sklearn.metrics import make_scorer, accuracy_score, r2_score, mean_squared_error
  import sklearn.metrics as skm
  from sklearn.metrics import classification_report
  
  
  from sklearn.tree import DecisionTreeClassifier
  # plt.style.use('seaborn-notebook')
  ## inline figures
  %matplotlib inline
  plt.style.use('seaborn')
  
  ## just to make sure few warnings are not shown
  import warnings
  warnings.filterwarnings("ignore")
  </code></pre>
  
  <p>I have tried commenting out <code>plt.style.use('seaborn')</code> but didn't work. Any suggestions would be appreciated</p>