Decision tree using sklearn

 

What is a Decision tree algorithm?

The decision tree Algorithm belongs to the family of supervised machine learning algorithms. It can be used for both a classification problem as well as for regression problem.

The goal of this algorithm is to create a model that predicts the value of a target variable, for which the decision tree uses the tree representation to solve the problem in which the leaf node corresponds to a class label and attributes are represented on the internal node of the tree.

We can use Scikit learn for the decision tree which makes it very easy to implement.

CODE:

import pandas as pd

from sklearn.tree import DecisionTreeClassifier 

from sklearn.model_selection import train_test_split 

from sklearn import metrics 

data = pd.read_csv("diabetes.csv") #Dataset

data.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	Pedigree	Age	Outcome
	6	148	72	35	0	33.6	0.627	50	1
	1	85	66	29	0	26.6	0.351	31	0
	8	183	64	0	0	23.3	0.672	32	1
	1	89	66	23	94	28.1	0.167	21	0
	0	137	40	35	168	43.1	2.288	33	1

features=['Pregnancies', 'Insulin', 'BMI', 'Age','Glucose','BloodPressure','Pedigree']

X = data[featurs] 

y = data.Outcome

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

clf = DecisionTreeClassifier()

clf = clf.fit(X_train,y_train)

y_pred = clf.predict(X_test)

print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

>>Accuracy: 0.6796536796536796