Python – How to Plot Learning Curves of Classifier

# Load the packages
import numpy as np
import pandas as pd
import Matplotlib.pyplot as plt

from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from sklearn import datasets

# Load the datasets
#
iris = datasets.load_iris()
X = iris.data
Y = iris.target

# Create training / test split; Note the stratification
# 
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1, stratify=Y)

# Perform feature scaling
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

# Fit / train the model
prcptrn = Perceptron(eta0=0.1, random_state=1)
prcptrn.fit(X_train_std, Y_train)

# Check the accuracy of the model
Y_predict_std = prcptrn.predict(X_test_std)
print("Accuracy Score %.3f" % accuracy_score(Y_test, Y_predict_std))

# Load the plot_learning_curves class
from mlxtend.plotting import plot_learning_curves

# Plot the learning curves
plot_learning_curves(X_train_std, Y_train, X_test_std, Y_test, prcptrn)