#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')


# # Plotting Learning Curves

# A function to plot learning curves for classifiers. Learning curves are extremely useful to analyze if a model is suffering from over- or under-fitting (high variance or high bias). The function can be imported via
# 

# > from mlxtend.plotting import plot_learning_curves

# ### References
# 
# -

# ## Example 1

# In[2]:


from mlxtend.plotting import plot_learning_curves
import matplotlib.pyplot as plt
from mlxtend.data import iris_data
from mlxtend.preprocessing import shuffle_arrays_unison
from sklearn.neighbors import KNeighborsClassifier
import numpy as np


# Loading some example data
X, y = iris_data()
X, y = shuffle_arrays_unison(arrays=[X, y], random_seed=123)
X_train, X_test = X[:100], X[100:]
y_train, y_test = y[:100], y[100:]

clf = KNeighborsClassifier(n_neighbors=5)

plot_learning_curves(X_train, y_train, X_test, y_test, clf)
plt.show()


# ## API

# In[1]:


with open('../../api_modules/mlxtend.plotting/plot_learning_curves.md', 'r') as f:
    print(f.read())