A decision Tree Is A Practical Method For Decision Making In Various Fields, Including Data Science, Artificial Intelligence, And Industrial Engineering.
This method is used for problems that have a large number of possible decisions and have different complexities.
Decision tree in the form of an algorithm
The decision tree consists of a series of nodes containing a decision or a condition. It starts from the tree’s root, and we reach other nodes by traversing different tree paths until we get a leaf representing the final result.
A decision tree can be used as a decision-making algorithm. In this algorithm, the input data set is checked for each node of the tree, and based on the condition or the decision in which the node is located, we are directed to one of the tree branches. This process continues until we reach a leaf that represents the final result. The advantages of using the decision tree can be mentioned in its simplicity in implementation, generalizability, and easy understanding.
What problems can be solved using a decision tree?
Among the critical issues that can be solved using the decision tree, the following should be mentioned:
- Disease diagnosis: decision tree can diagnose the disease using the patient’s clinical symptoms.
- Decision-making in marketing: Decisions can be made in the field of marketing using the decision tree. For example, by analyzing customer data, decisions can be made regarding discounts, pricing, etc.
- Housing price estimation: You can estimate the housing price using the decision tree. For example, by considering the different housing characteristics, it is possible to calculate their cost.
- Text classification detection: Text classification can be detected using a decision tree. For example, by analyzing the words in the text, they can be placed in one of the defined categories.
- Face recognition: Face recognition can be done using a decision tree. For example, it is possible to recognize a person’s face by analyzing different facial features.
In general, the decision tree is quite helpful in solving various problems that require a decision.
Decision tree in machine learning
A decision tree is an essential machine-learning method for solving classification and prediction problems. In this method, a data set is given to train a decision tree algorithm. Then, using this tree, you can make predictions or classify the data.
A decision tree is built using a series of decisions or conditions. Each tree node contains a situation used to make decisions about the data. For each state, the training set data is divided into two categories. Then, using unique algorithms, the best shape for data separation is selected and placed in the tree node.
Typically, the decision tree is constructed recursively, meaning that for each node in the tree, a new subtree is built that contains the data split into two categories by the node’s condition. This process is repeated until we reach an end condition, and the data is not separated into two groups.
A decision tree is very useful in machine learning because it can be efficient as a fast and understandable algorithm with big data. Also, the decision tree is easy to interpret and analyze and can be used for forecasting and classification problems such as disease diagnosis, sales forecasting, and marketing decision-making.
Can decision trees be used for prediction and classification problems of non-numerical data?
The answer is yes. Decision trees generally apply to discrete data (data with qualitative variables, such as color, type, etc.) and continuous data (data with quantitative variables, such as price, length, etc.). Decision trees can also be used for prediction problems and classification of non-numerical data.
For discrete data, a decision tree can determine conditions for each qualitative variable and then divide the data set into different categories based on these conditions. For example, in a text classification problem, the decision tree can determine a need for each word that divides the text into two categories.
For continuous data, a decision tree can determine conditions for dividing the data into different categories using visual values of quantitative variables. For example, in a housing price prediction problem, the decision tree can determine conditions for dividing the housing into two categories using price values.
The decision tree as a classification and prediction algorithm can generally work with discrete and continuous data. Hence, the possibility of using it in connection with various issues such as disease diagnosis, sales forecasting, and marketing decision-making is practical.
How to implement a decision tree in machine learning with Python?
Now that we have the general information about the decision tree let’s mention a practical example in this field. Decision tree implementation in machine learning is possible by various libraries, including scikit-learn in Python. Here’s a simple example of a decision tree implementation using the scikit-learn library in Python:
First, you need to install the scikit-learn library using the following command:
Pip install scikit-learn
Then, you can use the DecisionTreeClassifier class of the scikit-learn library to build a decision tree model. The following example shows how to make a decision tree model using training data and use it for prediction:
From sklearn.tree import DecisionTreeClassifier
Construction of training data
X_train = [[0, 0], [1, 1]]
y_train = [0, 1]
Building a decision tree model
calf = DecisionTreeClassifier()
# Training the model with training data
self.fit(X_train, y_train)
# Predict new data tag
X_test = [[2., 2.]]
y_pred = calf.predict(X_test)
In this example, the training data consists of two 2D features with labels in the y_train list. After that, a decision tree model is built using the DecisionTreeClassifier class and trained with the training data. Then, using the new test data, its label is obtained using the prediction of the decision tree model.
It should be noted that this example is straightforward, and in more complex cases, other factors, such as data preprocessing, parameter setting, etc., should be taken into account.
How to implement a decision tree in machine learning with C Plus Plus?
In C++, you can implement a decision tree using various libraries. One of the suitable libraries for this work is the open-source library.
To use MLPACK in C++, you must first install it. For this, you can use a package management system like apt-get:
Sudo apt-get install libmlpack-dev
Then, to build a decision tree using MLPACK, you need to implement code similar to the following:
#include <mlpack/core.hpp>
#include <mlpack/methods/decision_tree/decision_tree.hpp>
using namespace mlpack;
using namespace mlpack::tree;
int main()
{
// Build training data
arma::mat data; // The training data is stored as a matrix
data::Load(“dataset.csv”, data); // We load the data file
arma::Row<size_t> labels; // Training tags
data::Load(“labels.csv”, labels); // We load the tags file
// Build the decision tree model
DecisionTree<> tree(data, labels); // Build a decision tree
// Save the decision tree model
data::Save(“decision_tree.xml”, “tree”, true);
// Predict new data label
arma::vec test point = …; // Define new data
size_t predicted class;
tree.Classify(test point, predicted class); // Predict new data label
return 0;
}
In this example, the training data and labels are loaded from the CSV file, and then a decision tree is built using the DecisionTree<> class. Then, using the Classify() function, you can predict the new data label. Finally, the decision tree model is saved to an XML file.
It should be noted that this example is also straightforward. As in the previous example, data preprocessing, setting of various parameters, etc., are required for more complex cases.
Should we always rebuild the decision tree to predict a new data label?
No, there is no need to rebuild the decision tree to predict the new data label. Once the decision tree is built with the training data, it is a model that can predict new data labels. In other words, a decision tree is a machine-learning model built using training data to predict new data labels.
To predict the label of new data using a decision tree, give the new data features as input to the model and predict the corresponding label using the decision tree. For example, in Python, you can predict new data labels using a decision tree model using the following code:
# The DecisionTreeClassifier model is assumed to be created with the name of.
# New data
new_data = [[0.2, 0.8], [0.5, 0.5], [0.7, 0.3]]
# New data label prediction
predicted_labels = calf.predict(new_data)
In this example, by giving new data as input to a decision tree model already built with training data, the labels for that data are predicted using the predict() function.
So, in general, there is no need to rebuild the decision tree to predict the new data label, and the model built with the training data can be used to predict the new data label.
Can I combine the decision tree model with other algorithms?
Yes, you can combine decision trees with other algorithms. This method is known as a hybrid training method, which is known as combining algorithms. In this method, several machine learning models, such as a decision tree, are built using the training data. Finally, their results are combined to get the best outcome for predicting new data labels.
There are two common ways to combine models:
- Voting method: In this method, the label that has obtained the highest number of votes in the machine learning models is selected as the final label to predict the new data label. This method is suitable for models with fast training and processing capabilities, such as decision trees.
- Averaging method: In this method, machine learning models combine the prediction results of new data labels, and their average is selected as the final label. This method is suitable for models that are slow to train and have high accuracy, such as neural networks.
You can use hybrid training methods such as the ensemble to combine the decision tree with other models. For example, you can combine the decision tree with algorithms such as Majority Voting or with other models such as neural networks.
Is the averaging method suitable for combining the decision tree with other models, such as SVM?
The averaging method is also suitable for combining the decision tree with other models, such as SVM. The averaging method for combining machine learning models is known as a combined training method, which is known as an ensemble or combination of algorithms.
In this method, machine learning models combine the new data label prediction results, and their average is selected as the final label. The advantage of this method is that blending the label prediction results of new data gives a higher label prediction accuracy.
You can use the averaging method to combine the decision tree with other models, such as SVM. In this method, the label prediction results by decision tree and SVM are combined to predict new data, and their average is selected as the final label.
For example, in Python, you can use the following code to combine a decision tree and SVM using the averaging method:
# Two DecisionTreeClassifier and SVC models are assumed to be built with clf1 and clf2.
# the training and new data are assumed to be stored in X_train and X_test variables, respectively.
# Teaching two models
clf1.fit(X_train, y_train)
clf2.fit(X_train, y_train)
# Predict new data labels using built-in models
y_pred1 = clf1.predict(X_test)
y_pred2 = clf2.predict(X_test)
# Combine label prediction results using averaging
y_pred = (y_pred1 + y_pred2) / 2