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

# ### From the Titanic Dataset
# ![](https://upload.wikimedia.org/wikipedia/commons/f/f3/CART_tree_titanic_survivors.png)
# <div style='text-align:right;'>By Stephen Milborrow (Own work) [CC BY-SA 3.0 via Wikimedia Commons]</div>

# In[1]:


import mglearn # credits to Muller and Guido (https://www.amazon.com/dp/1449369413/)
import matplotlib.pyplot as plt
import numpy as np
get_ipython().run_line_magic('matplotlib', 'inline')

mglearn.plots.plot_tree_not_monotone()


# In[2]:


from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target, random_state=42)
tree = DecisionTreeClassifier(random_state=0)
tree.fit(X_train, y_train)

print('Accuracy on the training subset: {:.3f}'.format(tree.score(X_train, y_train)))
print('Accuracy on the test subset: {:.3f}'.format(tree.score(X_test, y_test)))


# In[3]:


tree = DecisionTreeClassifier(max_depth=4, random_state=0)
tree.fit(X_train, y_train)

print('Accuracy on the training subset: {:.3f}'.format(tree.score(X_train, y_train)))
print('Accuracy on the test subset: {:.3f}'.format(tree.score(X_test, y_test)))


# In[4]:


import graphviz
from sklearn.tree import export_graphviz

export_graphviz(tree, out_file='cancertree.dot', class_names=['malignant', 'benign'], feature_names=cancer.feature_names,
               impurity=False, filled=True)


# ![](cancertree.png)

# In[5]:


print('Feature importances: {}'.format(tree.feature_importances_))
type(tree.feature_importances_)


# In[6]:


print(cancer.feature_names)


# In[7]:


n_features = cancer.data.shape[1]
plt.barh(range(n_features), tree.feature_importances_, align='center')
plt.yticks(np.arange(n_features), cancer.feature_names)
plt.xlabel('Feature Importance')
plt.ylabel('Feature')
plt.show()


# ### Advantages of Decision Trees
# 
#  - easy to view and understand
#  - no need to pre-process, normalize, scale, and/or standardize features
#  
# ### Paramaters to work with
#  
#  - max_depth
#  - min_samples_leaf, max_samples_leaf
#  - max_leaf_nodes
#  - etc.
#  
# ### Main Disadvantages
# 
# - tendency to overfit
# - poor generalization 
# 
# ####  <center> Possible work-around: ensembles of decision trees </center>

# In[ ]: