
import pandas as pd 
import numpy as np
from sklearn.datasets import load_iris
from sklearn import tree


iris=load_iris()


print(iris.feature_names)

['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']


print(iris.target_names)

['setosa' 'versicolor' 'virginica']


#Spilitting the dataset
removed =[0,50,100]
new_target = np.delete(iris.target,removed)
new_data = np.delete(iris.data,removed, axis=0)


#train classifier
clf = tree.DecisionTreeClassifier() # defining decision tree classifier
clf=clf.fit(new_data,new_target) # train data on new data and new target
prediction = clf.predict(iris.data[removed]) #  assign removed data as input


print("Original Labels",iris.target[removed])
print("Labels Predicted",prediction)

Original Labels [0 1 2]
Labels Predicted [0 1 2]


tree.plot_tree(clf)

[Text(167.4, 199.32, 'X[3] <= 0.8\ngini = 0.667\nsamples = 147\nvalue = [49, 49, 49]'),
 Text(141.64615384615385, 163.07999999999998, 'gini = 0.0\nsamples = 49\nvalue = [49, 0, 0]'),
 Text(193.15384615384616, 163.07999999999998, 'X[3] <= 1.75\ngini = 0.5\nsamples = 98\nvalue = [0, 49, 49]'),
 Text(103.01538461538462, 126.83999999999999, 'X[2] <= 4.95\ngini = 0.171\nsamples = 53\nvalue = [0, 48, 5]'),
 Text(51.50769230769231, 90.6, 'X[3] <= 1.65\ngini = 0.042\nsamples = 47\nvalue = [0, 46, 1]'),
 Text(25.753846153846155, 54.359999999999985, 'gini = 0.0\nsamples = 46\nvalue = [0, 46, 0]'),
 Text(77.26153846153846, 54.359999999999985, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(154.52307692307693, 90.6, 'X[3] <= 1.55\ngini = 0.444\nsamples = 6\nvalue = [0, 2, 4]'),
 Text(128.76923076923077, 54.359999999999985, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 3]'),
 Text(180.27692307692308, 54.359999999999985, 'X[2] <= 5.45\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1]'),
 Text(154.52307692307693, 18.119999999999976, 'gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]'),
 Text(206.03076923076924, 18.119999999999976, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(283.2923076923077, 126.83999999999999, 'X[2] <= 4.85\ngini = 0.043\nsamples = 45\nvalue = [0, 1, 44]'),
 Text(257.53846153846155, 90.6, 'X[1] <= 3.1\ngini = 0.444\nsamples = 3\nvalue = [0, 1, 2]'),
 Text(231.7846153846154, 54.359999999999985, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]'),
 Text(283.2923076923077, 54.359999999999985, 'gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]'),
 Text(309.04615384615386, 90.6, 'gini = 0.0\nsamples = 42\nvalue = [0, 0, 42]')]


#Splitting the dataset into training and testing variables
from sklearn.model_selection import train_test_split
 
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2,random_state=20)
 
#keeping 80% as training data and 20% as testing data.


X_train

array([[5.7, 3. , 4.2, 1.2],
       [5.6, 3. , 4.1, 1.3],
       [5.1, 3.5, 1.4, 0.2],
       [5.1, 3.8, 1.6, 0.2],
       [4.8, 3.4, 1.6, 0.2],
       [4.9, 2.5, 4.5, 1.7],
       [6. , 3.4, 4.5, 1.6],
       [4.9, 3. , 1.4, 0.2],
       [6.4, 3.2, 4.5, 1.5],
       [7.4, 2.8, 6.1, 1.9],
       [5.7, 2.8, 4.5, 1.3],
       [6.1, 2.6, 5.6, 1.4],
       [4.9, 3.6, 1.4, 0.1],
       [6.7, 3.1, 4.4, 1.4],
       [6.3, 3.3, 4.7, 1.6],
       [4.7, 3.2, 1.3, 0.2],
       [5.4, 3.9, 1.7, 0.4],
       [5. , 3.3, 1.4, 0.2],
       [5.7, 2.5, 5. , 2. ],
       [5.1, 3.8, 1.9, 0.4],
       [6.4, 2.8, 5.6, 2.2],
       [6.2, 3.4, 5.4, 2.3],
       [5.4, 3.4, 1.5, 0.4],
       [6.3, 3.3, 6. , 2.5],
       [6. , 2.2, 4. , 1. ],
       [5.6, 2.9, 3.6, 1.3],
       [5.6, 3. , 4.5, 1.5],
       [4.5, 2.3, 1.3, 0.3],
       [5.1, 3.4, 1.5, 0.2],
       [5.5, 2.4, 3.8, 1.1],
       [6.2, 2.9, 4.3, 1.3],
       [5.8, 2.6, 4. , 1.2],
       [5.6, 2.5, 3.9, 1.1],
       [6.7, 3.1, 4.7, 1.5],
       [6.5, 2.8, 4.6, 1.5],
       [5.7, 2.9, 4.2, 1.3],
       [5.1, 3.3, 1.7, 0.5],
       [6.2, 2.2, 4.5, 1.5],
       [6.3, 2.3, 4.4, 1.3],
       [4.9, 3.1, 1.5, 0.1],
       [4.4, 3. , 1.3, 0.2],
       [5.4, 3. , 4.5, 1.5],
       [5.5, 2.3, 4. , 1.3],
       [6.1, 3. , 4.6, 1.4],
       [5.1, 3.8, 1.5, 0.3],
       [5.3, 3.7, 1.5, 0.2],
       [6.4, 3.2, 5.3, 2.3],
       [5.5, 3.5, 1.3, 0.2],
       [4.8, 3.1, 1.6, 0.2],
       [5. , 2. , 3.5, 1. ],
       [6. , 3. , 4.8, 1.8],
       [5.8, 2.7, 5.1, 1.9],
       [5.6, 2.8, 4.9, 2. ],
       [6.3, 2.9, 5.6, 1.8],
       [6.4, 2.8, 5.6, 2.1],
       [7.1, 3. , 5.9, 2.1],
       [5.8, 2.7, 3.9, 1.2],
       [6.5, 3. , 5.5, 1.8],
       [7.7, 3. , 6.1, 2.3],
       [7.2, 3.6, 6.1, 2.5],
       [4.4, 2.9, 1.4, 0.2],
       [6.8, 3. , 5.5, 2.1],
       [5.2, 3.5, 1.5, 0.2],
       [5. , 3.6, 1.4, 0.2],
       [5.8, 4. , 1.2, 0.2],
       [6.7, 3. , 5.2, 2.3],
       [6.9, 3.1, 4.9, 1.5],
       [5.9, 3.2, 4.8, 1.8],
       [6.9, 3.1, 5.4, 2.1],
       [6.3, 2.7, 4.9, 1.8],
       [5.6, 2.7, 4.2, 1.3],
       [4.4, 3.2, 1.3, 0.2],
       [6.1, 3. , 4.9, 1.8],
       [5.5, 4.2, 1.4, 0.2],
       [5. , 2.3, 3.3, 1. ],
       [7.7, 3.8, 6.7, 2.2],
       [4.8, 3.4, 1.9, 0.2],
       [6. , 2.9, 4.5, 1.5],
       [6.1, 2.9, 4.7, 1.4],
       [6.2, 2.8, 4.8, 1.8],
       [4.8, 3. , 1.4, 0.3],
       [4.7, 3.2, 1.6, 0.2],
       [7.2, 3.2, 6. , 1.8],
       [6.7, 3.3, 5.7, 2.5],
       [5.2, 2.7, 3.9, 1.4],
       [5.5, 2.6, 4.4, 1.2],
       [5.7, 2.6, 3.5, 1. ],
       [6.7, 3.3, 5.7, 2.1],
       [6.7, 3.1, 5.6, 2.4],
       [5. , 3.5, 1.6, 0.6],
       [6.9, 3.2, 5.7, 2.3],
       [5.1, 3.5, 1.4, 0.3],
       [6.7, 3. , 5. , 1.7],
       [4.6, 3.1, 1.5, 0.2],
       [5.7, 3.8, 1.7, 0.3],
       [6.3, 2.5, 5. , 1.9],
       [6.3, 3.4, 5.6, 2.4],
       [7.3, 2.9, 6.3, 1.8],
       [5.4, 3.7, 1.5, 0.2],
       [5.2, 4.1, 1.5, 0.1],
       [6.5, 3.2, 5.1, 2. ],
       [6. , 2.7, 5.1, 1.6],
       [5.9, 3. , 4.2, 1.5],
       [7.9, 3.8, 6.4, 2. ],
       [5. , 3. , 1.6, 0.2],
       [6.9, 3.1, 5.1, 2.3],
       [4.6, 3.4, 1.4, 0.3],
       [5. , 3.4, 1.5, 0.2],
       [5.8, 2.7, 5.1, 1.9],
       [7.6, 3. , 6.6, 2.1],
       [5.4, 3.9, 1.3, 0.4],
       [5. , 3.4, 1.6, 0.4],
       [5. , 3.5, 1.3, 0.3],
       [7.7, 2.6, 6.9, 2.3],
       [6.1, 2.8, 4. , 1.3],
       [4.6, 3.6, 1. , 0.2],
       [6.6, 3. , 4.4, 1.4],
       [6.4, 3.1, 5.5, 1.8],
       [5.7, 4.4, 1.5, 0.4],
       [5.7, 2.8, 4.1, 1.3]])


X_test

array([[4.6, 3.2, 1.4, 0.2],
       [6.1, 2.8, 4.7, 1.2],
       [6.4, 2.9, 4.3, 1.3],
       [7.2, 3. , 5.8, 1.6],
       [5.8, 2.7, 4.1, 1. ],
       [5.5, 2.5, 4. , 1.3],
       [6.8, 3.2, 5.9, 2.3],
       [5.1, 3.7, 1.5, 0.4],
       [6.7, 2.5, 5.8, 1.8],
       [4.8, 3. , 1.4, 0.1],
       [6.5, 3. , 5.2, 2. ],
       [6.8, 2.8, 4.8, 1.4],
       [6. , 2.2, 5. , 1.5],
       [5. , 3.2, 1.2, 0.2],
       [5.2, 3.4, 1.4, 0.2],
       [7.7, 2.8, 6.7, 2. ],
       [4.3, 3. , 1.1, 0.1],
       [6.6, 2.9, 4.6, 1.3],
       [5.8, 2.8, 5.1, 2.4],
       [4.9, 2.4, 3.3, 1. ],
       [7. , 3.2, 4.7, 1.4],
       [5.9, 3. , 5.1, 1.8],
       [6.4, 2.7, 5.3, 1.9],
       [5.4, 3.4, 1.7, 0.2],
       [6.3, 2.5, 4.9, 1.5],
       [5.5, 2.4, 3.7, 1. ],
       [5.1, 2.5, 3. , 1.1],
       [4.9, 3.1, 1.5, 0.2],
       [6.5, 3. , 5.8, 2.2],
       [6.3, 2.8, 5.1, 1.5]])


clf.fit(X_train, y_train)

DecisionTreeClassifier()


clf.score(X_test, y_test)

0.9333333333333333

First, import the required libraries¶

Now, load the iris dataset¶

Remove the Labels¶

Train the Decision Tree Classifier¶