Decisiontree.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import tree
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
X,y=load_breast_cancer(return_X_y=True)
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0)
clf=DecisionTreeClassifier(random_state=0)
clf.fit(X_train,y_train)
y_train_predicted=clf.predict(X_train)
y_test_predicted=clf.predict(X_test)
train_acc=accuracy_score(y_train,y_train_predicted)
test_acc=accuracy_score(y_test,y_test_predicted)
print("accuracy of training dataset:",train_acc)
print("accuracy of test dataset:",test_acc)
plt.figure(figsize=(16,8))
tree.plot_tree(clf)
plt.show()
# Post-Pruning
path=clf.cost_complexity_pruning_path(X_train,y_train)
ccp_alphas,impurities=path.ccp_alphas,path.impurities
print("ccp alpha wil give list of values :",ccp_alphas)
print("***********************************************************")
print("Impurities in Decision Tree :",impurities)
clfs=[]
for ccp_alpha in ccp_alphas:
clf=DecisionTreeClassifier(random_state=0,ccp_alpha=ccp_alpha)
clf.fit(X_train,y_train)
clfs.append(clf)
print("Last node in Decision tree is {} and ccp_alpha for last node is {}".format(clfs[-
1].tree_.node_count,ccp_alphas[-1]))
train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]
fig, ax = plt.subplots()
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_scores, marker='o', label="train",drawstyle="steps-post")
ax.plot(ccp_alphas, test_scores, marker='o', label="test",drawstyle="steps-post")
ax.legend()
plt.show()
clf=DecisionTreeClassifier(random_state=0,ccp_alpha=0.02)
clf.fit(X_train,y_train)
plt.figure(figsize=(12,8))
tree.plot_tree(clf,rounded=True,filled=True)
plt.show()
acc=accuracy_score(y_test,clf.predict(X_test))
print("accuracy of post-pruning operation:",acc)
clf=DecisionTreeClassifier(criterion= 'gini',max_depth= 17,min_samples_leaf=
3,min_samples_split=
12,splitter= 'random')
clf.fit(X_train,y_train)
plt.figure(figsize=(20,12))
tree.plot_tree(clf,rounded=True,filled=True)
plt.show()
y_predicted=clf.predict(X_test)
accuracy=accuracy_score(y_test,y_predicted)
print("accuracy of pre-pruning operation:",accuracy)