Plot Decision Tree train/test accuracy against max depth

I was trying to plot the accuracy of my train and test set from a decision tree model. Since I am new to using python, I wasn’t sure what type of graphing package I should use. I have used a simple for loop for getting the printed results, but not sure how ]I can plot it.

Thanks!

My code:

for x in max_depth_list :

  dtc =DecisionTreeClassifier(max_depth=x)
  dtc.fit(train_x,train_y)

  train_z = dtc.predict(train_x)
  train_z_prob = dtc.predict_proba(train_x)[:,1]

  test_z = dtc.predict(test_x)
  test_z_prob = dtc.predict_proba(test_x)[:,1]

  print("split: {}".format(x))
  print("model accuracy: {}".format(accuracy_score(test_y, test_z)))

Desired graph enter image description here

Answer

The plot in the image you posted was most likely created with the matplotlib.pyplot module. You can probably plot a similar graph by executing something like this, assuming that you have imported other necessary dependencies:

import numpy as np
import matplotlib.pyplot as plt

max_depth_list = [1,2,3,4]

train_errors = [] # Log training errors for each model
test_errors = [] # Log testing errors for each model

for x in max_depth_list:
    dtc = DecisionTreeClassifier(max_depth=x) 
    dtc.fit(train_x,train_y)
    train_z = dtc.predict(train_x)
    test_z = dtc.predict(test_x)
    train_errors.append(accuracy_score(train_x, train_z))
    test_errors.append(accuracy_score(test_y, test_z))

x = np.arange(len(max_depth_list)) + 1 # Create domain for plot
plt.plot(x, train_errors, label='Training Error') # Plot training error over domain
plt.plot(x, test_errors, label='Testing Error') # Plot testing error over domain
plt.xlabel('Maximum Depth') # Label x-axis
plt.ylabel('Total Error') # Label y-axis
plt.legend() # Show plot labels as legend
plt.show() # Show graph

I’m new to this community as well, so I am in no position to give advice to other users. However, it’s probably a good idea to format your source code for better readability and presentation. Just a heads up.

I hope this helps. Let me know if anything is unclear.