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09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 2/17 Decision Tree Classification Algorithm Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome. In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. The decisions or the test are performed on the basis of features of the given dataset. It is a graphical representation for getting all the possible solutions to a problem/decision based on given conditions. It is called a decision tree because, similar to a tree, it starts with the root node, which expands on further branches and constructs a tree-like structure. In order to build a tree, we use the CART algorithm, which stands for Classification and Regression Tree algorithm. A decision tree simply asks a question, and based on the answer (Yes/No), it further split the tree into subtrees. Below diagram explains the general structure of a decision tree: Note: A decision tree can contain categorical data (YES/NO) as well as numeric data.

09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 4/17 Step-1: Begin the tree with the root node, says S, which contains the complete dataset. Step-2: Find the best attribute in the dataset using Attribute Selection Measure (ASM). Step-3: Divide the S into subsets that contains possible values for the best attributes. Step-4: Generate the decision tree node, which contains the best attribute. Step-5: Recursively make new decision trees using the subsets of the dataset created in step -3. Continue this process until a stage is reached where you cannot further classify the nodes and called the final node as a leaf node. Example: Suppose there is a candidate who has a job offer and wants to decide whether he should accept the offer or Not. So, to solve this problem, the decision tree starts with the root node (Salary attribute by ASM). The root node splits further into the next decision node (distance from the office) and one leaf node based on the corresponding labels. The next decision node further gets split into one decision node (Cab facility) and one leaf node. Finally, the decision node splits into two leaf nodes (Accepted offers and Declined offer). Consider the below diagram: Attribute Selection Measures While implementing a Decision tree, the main issue arises that how to select the best attribute for the root node and for sub-nodes. So, to solve such problems there is a technique which is called as Attribute selection measure or ASM. By this measurement, we can easily select the best attribute for the nodes of the tree. There are two popular techniques for ASM, which are: Information Gain

09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 5/17 Gini Index 1. Information Gain: Information gain is the measurement of changes in entropy after the segmentation of a dataset based on an attribute. It calculates how much information a feature provides us about a class. According to the value of information gain, we split the node and build the decision tree. A decision tree algorithm always tries to maximize the value of information gain, and a node/attribute having the highest information gain is split first. It can be calculated using the below formula: Information Gain= Entropy(S)- [(Weighted Avg) *Entropy(each feature) Entropy: Entropy is a metric to measure the impurity in a given attribute. It specifies randomness in data. Entropy can be calculated as: Entropy(s)= -P(yes)log2 P(yes)- P(no) log2 P(no) Where, S= Total number of samples P(yes)= probability of yes P(no)= probability of no 2. Gini Index: Gini index is a measure of impurity or purity used while creating a decision tree in the CART(Classification and Regression Tree) algorithm. An attribute with the low Gini index should be preferred as compared to the high Gini index. It only creates binary splits, and the CART algorithm uses the Gini index to create binary splits. Gini index can be calculated using the below formula: Gini Index= 1- ∑ j P j 2

09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 7/17 Data Pre-processing step Fitting a Decision-Tree algorithm to the Training set Predicting the test result Test accuracy of the result(Creation of Confusion matrix) Visualizing the test set result. 1. Data Pre-Processing Step: Below is the code for the pre-processing step: # importing libraries import numpy as nm import matplotlib.pyplot as mtp import pandas as pd #importing datasets data_set= pd.read_csv( 'user_data.csv' ) #Extracting Independent and dependent Variable x= data_set.iloc[:, [ 2 , 3 ]].values y= data_set.iloc[:, 4 ].values # Splitting the dataset into training and test set. from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.25 , random_state= 0 ) #feature Scaling from sklearn.preprocessing import StandardScaler st_x= StandardScaler() x_train= st_x.fit_transform(x_train) x_test= st_x.transform(x_test)

09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 8/17 In the above code, we have pre-processed the data. Where we have loaded the dataset, which is given as: 2. Fitting a Decision-Tree algorithm to the Training set Now we will fit the model to the training set. For this, we will import the DecisionTreeClassifier class from sklearn.tree library. Below is the code for it: #Fitting Decision Tree classifier to the training set From sklearn.tree import DecisionTreeClassifier classifier= DecisionTreeClassifier(criterion= 'entropy' , random_state= 0 ) classifier.fit(x_train, y_train) In the above code, we have created a classifier object, in which we have passed two main parameters; "criterion='entropy': Criterion is used to measure the quality of split, which is calculated by information gain given by entropy.

09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 10/17 4. Test accuracy of the result (Creation of Confusion matrix) In the above output, we have seen that there were some incorrect predictions, so if we want to know the number of correct and incorrect predictions, we need to use the confusion matrix. Below is the code for it: #Creating the Confusion matrix from sklearn.metrics import confusion_matrix cm= confusion_matrix(y_test, y_pred) Output:

09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 11/17 In the above output image, we can see the confusion matrix, which has 6+3= 9 incorrect predictions and 62+29=91 correct predictions. Therefore, we can say that compared to other classification models, the Decision Tree classifier made a good prediction. 5. Visualizing the training set result: Here we will visualize the training set result. To visualize the training set result we will plot a graph for the decision tree classifier. The classifier will predict yes or No for the users who have either Purchased or Not purchased the SUV car as we did in Logistic Regression. Below is the code for it: #Visulaizing the trianing set result from matplotlib.colors import ListedColormap x_set, y_set = x_train, y_train x1, x2 = nm.meshgrid(nm.arange(start = x_set[:, 0 ].min() - 1 , stop = x_set[:, 0 ].max() + 1 , step = 0.01 ) nm.arange(start = x_set[:, 1 ].min() - 1 , stop = x_set[:, 1 ].max() + 1 , step = 0.01 )) mtp.contourf(x1, x2, classifier.predict(nm.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape), alpha = 0.75 , cmap = ListedColormap(( 'purple' , 'green' ))) mtp.xlim(x1.min(), x1.max()) mtp.ylim(x2.min(), x2.max()) fori, j in enumerate(nm.unique(y_set)): mtp.scatter(x_set[y_set == j, 0 ], x_set[y_set == j, 1 ], c = ListedColormap(( 'purple' , 'green' ))(i), label = j) mtp.title( 'Decision Tree Algorithm (Training set)' ) mtp.xlabel( 'Age' ) mtp.ylabel( 'Estimated Salary' ) mtp.legend()

09/01/2023, 21:10 Decision Tree Algorithm in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm 13/17 ← Prev Next → mtp.ylabel( 'Estimated Salary' ) mtp.legend() mtp.show() Output: As we can see in the above image that there are some green data points within the purple region and vice versa. So, these are the incorrect predictions which we have discussed in the confusion matrix. A 26-Year-Old Girl From Ahmedabad Became A Millionaire Overnight Olymp Trade Youtube For Videos Join Our Youtube Channel: Join Now

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