Decision Trees and Random Forests classifier in Python

Welcome everyone, Today we will see Decision Trees and Random Forests classifier-and Types in Python so let’s start:

Import Libraries


import pandas as pd


import numpy as np


import matplotlib.pyplot as plt


import seaborn as sns


%matplotlib inline

Get the Data

df = pd.read_csv('Decision Trees.csv')

df.head()

Decision  Age Number Start


0 present 34 3 9


1 absent 58 4 15


2 absent 28 5 8


3 present 72 3 4


4 absent 81 4 15

Split data into x/y_training and x/y_test data.

Let’s start to split up the data into a training and test set.

from sklearn.model_selection import train_test_split


x = df.drop('Decision Trees',axis=1)


y = df['Decision Trees']


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20)

check out Decision Trees

We will start just by training a single decision trees in this section.

from sklearn.tree import DecisionTreeClassifier


dtree = DecisionTreeClassifier()


dtree.fit(x_train,y_train)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,


            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,


            min_samples_split=2, min_weight_fraction_leaf=0.0,


            presort=False, random_state=None, splitter='best')

How to Prediction and Evaluation the data
Let’s start to evaluate our decision tree.

predictions = dtree.predict(x_test)


from sklearn.metrics import classification_report,confusion_matrix


print(classification_report(y_test,predictions))

         precisionx    recallx  f1-scorex   supportx


    present       0.80      0.80      0.80        15


    absent       0.45      0.45      0.45         10


avg / total       0.75      0.75      0.75        25

print(confusion_matrix(y_test,predictions))


[[18  4]


 [ 2  3]

Tree Visualization

from IPython.display import Image  


from sklearn.externals.six import StringIO  


from sklearn.tree import export_graphviz


import pydot 

features = list(df.columns[1:])


features


['Ages', 'Numbers', 'Starts']


dot_data = StringIO()  


export_graphviz(dtree, out_file=dot_data,feature_names=features,filled=True,rounded=True)


graph = pydot.graph_from_dot_data(dot_data.getvalue())  


Image(graph[0].create_png())  

Decision Trees and Random Forests classifier-Types in Python

Random Forests
Now This time to compare the decision tree model to a random forest.

from sklearn.ensemble import RandomForestClassifier


rfc = RandomForestClassifier(n_estimators=100)


rfc.fit(x_train, y_train)

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',


            max_depth=None, max_features='auto', max_leaf_nodes=None,


            min_samples_leaf=1, min_samples_split=2,


            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,


            oob_score=False, random_state=None, verbose=0,


            warm_start=False)


rfc_pred = rfc.predict(X_test)

print(confusion_matrix(y_test,rfc_pred))


[[14  3]


 [ 2  4]]


print(classification_report(y_test,rfc_pred))

             precisionx    recallx  f1-scorex   supportx


    present     0.83      0.80      0.78        10


    absent      0.55      0.50      0.54         15


avg / total       0.75      0.70      0.77        25

How many types of decision trees in machine learning?
It’s based on the type of target variable in python.  
1. Categorical variable Decision Tree: If in Decision Tree which has a available categorical target variable then it called a Categorical variable decision tree in python.
Summary:
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