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Decision tree implementation using Python
Decision tree is an algorithm which is mainly applied to data classification scenarios. It is a tree structure where each node represents the features and each edge represents the decision taken. Starting from the root node we go on evaluating the features for classification and take a decision to follow a specific edge. Whenever a new data point comes in , this same method is applied again and again and then the final conclusion is taken when all the required features are studied or applied to the classification scenario. So Decision tree algorithm is a supervised learning model used in predicting a dependent variable with a series of training variables.
Example
We will take the drug test data available at kaggle. As a first step we will read the data from a csv file using pandas and see it content and structure.
import pandas as pd
datainput = pd.read_csv("drug.csv", delimiter=",") #https://www.kaggle.com/gangliu/drugsets
print(datainput)Running the above code gives us the following result:
Age Sex BP Cholesterol Na_to_K Drug 0 23 F HIGH HIGH 25.355 drugY 1 47 M LOW HIGH 13.093 drugC 2 47 M LOW HIGH 10.114 drugC 3 28 F NORMAL HIGH 7.798 drugX 4 61 F LOW HIGH 18.043 drugY .. ... .. ... ... ... ... 195 56 F LOW HIGH 11.567 drugC 196 16 M LOW HIGH 12.006 drugC 197 52 M NORMAL HIGH 9.894 drugX [200 rows x 6 columns]
Pre-processing the data
In the next step we pre-process the above data to get numeric values for different text values we have in the data. That is useful to train and test the sample data about the decision to use certain drug for a given value of age, sex, BP etc.
Example
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
datainput = pd.read_csv("drug.csv", delimiter=",")
X = datainput[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
from sklearn import preprocessing
label_gender = preprocessing.LabelEncoder()
label_gender.fit(['F','M'])
X[:,1] = label_gender.transform(X[:,1])
label_BP = preprocessing.LabelEncoder()
label_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])
X[:,2] = label_BP.transform(X[:,2])
label_Chol = preprocessing.LabelEncoder()
label_Chol.fit([ 'NORMAL', 'HIGH'])
X[:,3] = label_Chol.transform(X[:,3])
# Printing the first 6 records
print(X[0:6])Running the above code gives us the following result -
[[23 0 0 0 25.355] [47 1 1 0 13.093] [47 1 1 0 10.113999999999999] [28 0 2 0 7.797999999999999] [61 0 1 0 18.043] [22 0 2 0 8.607000000000001] ]
Converting the Dependent variable
Next we also convert the dependent variable into numerical values so that it can be used in the training as well as the evaluation data set.
Example
import pandas as pd
datainput = pd.read_csv("drug.csv", delimiter=",")
X = datainput[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
y = datainput["Drug"]
print(y[0:6])Output
Running the above code gives us the following result:
0 drugY 1 drugC 2 drugC 3 drugX 4 drugY 5 drugX Name: Drug, dtype: object
Training the Dataset
Next we use 30 percent of the supplied data as a training data set. This will be use as the basis for creating the classification for the remaining 70 percentages which we will call as test data.
Example
import pandas as pd
from sklearn.model_selection import train_test_split
datainput = pd.read_csv("drug.csv", delimiter=",")
X = datainput[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
y = datainput["Drug"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=3)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)Output
Running the above code gives us the following result:
(140, 5) (60, 5) (140,) (60,)
Getting the result from Trained Data set
Next we can apply the decision tree to see the result for the trained data set. Here we create a tree based on the input we have and using a criteria called entropy. And finally we calculate the accuracy of the decision tree.
Example
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
datainput = pd.read_csv("drug.csv", delimiter=",")
X = datainput[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
# Data Preprocessing
from sklearn import preprocessing
label_gender = preprocessing.LabelEncoder()
label_gender.fit(['F', 'M'])
X[:, 1] = label_gender.transform(X[:, 1])
label_BP = preprocessing.LabelEncoder()
label_BP.fit(['LOW', 'NORMAL', 'HIGH'])
X[:, 2] = label_BP.transform(X[:, 2])
label_Chol = preprocessing.LabelEncoder()
label_Chol.fit(['NORMAL', 'HIGH'])
X[:, 3] = label_Chol.transform(X[:, 3])
y = datainput["Drug"]
# train_test_split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=3)
drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
drugTree.fit(X_train, y_train)
predicted = drugTree.predict(X_test)
print(predicted)
print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y_test, predicted))Output
Running the above code gives us the following result:
['drugY' 'drugX' 'drugX' 'drugX' 'drugX' 'drugC' 'drugY' 'drugA' 'drugB' 'drugA' 'drugY' 'drugA' 'drugY' 'drugY' 'drugX' 'drugY' 'drugX' 'drugX' 'drugB' 'drugX' 'drugX' 'drugY' 'drugY' 'drugY' 'drugX' 'drugB' 'drugY' 'drugY' 'drugA' 'drugX' 'drugB' 'drugC' 'drugC' 'drugX' 'drugX' 'drugC' 'drugY' 'drugX' 'drugX' 'drugX' 'drugA' 'drugY' 'drugC' 'drugY' 'drugA' 'drugY' 'drugY' 'drugY' 'drugY' 'drugY' 'drugB' 'drugX' 'drugY' 'drugX' 'drugY' 'drugY' 'drugA' 'drugX' 'drugY' 'drugX'] DecisionTrees's Accuracy: 0.9833333333333333
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