Decision tree python code example. In this case, the accuracy is 80.

Second, create an object that will contain your rules. A decision tree follows a set of if-else conditions to visualize the data and classify it according to the conditions. Apr 17, 2022 · In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. Apr 14, 2021 · Apologies, but something went wrong on our end. The code and the data are available at GitHub. We can use decision tree for both No Active Events. Oct 30, 2019 · Trained decision tree. When making a prediction for a new data point, the algorithm traverses the decision tree from the root node to a leaf node based on the feature values Click here to buy the book for 70% off now. One option is to use the decision tree classifier in Spark - in which you can explicitly declare the categorical features and their ordinality. Observations are represented in branches and conclusions are represented in leaves. There are different algorithms to generate them, such as ID3, C4. Let Examples vi, be the subset of Examples that have value vi for A. First Approach (In case of a single feature) Naive Bayes classifier calculates the probability of an event in the following steps: Step 1: Calculate the prior probability for given class labels. Jan 1, 2023 · Training a decision tree is relatively expensive. For example, if the training dataset has 100 rows, the max_samples argument could be set to 0. Feb 5, 2022 · For the first decision tree, it may choose only feature 1 and feature 2; For the second decision tree, it uses the different pair of features, e. model_selection import train_test_split. It is one of the most widely used and practical methods for supervised learning. x = scale (x) y = scale (y)xtrain, xtest, ytrain, ytest=train_test_split (x, y, test_size=0. Feb 18, 2023 · To begin, we import all of the libraries that will be needed in this example, including DecisionTreeRegressor. Colab shows that the root condition contains 243 examples. In the proceeding example, we’ll be using a dataset that categories people as attractive or not based on certain features. The maximum is given by the number of instances in the training set. 2 Random Forest. Let’s see the Step-by-Step implementation –. The first article was about Decision Trees. Updated Jun 2024 · 12 minread. Let’s get started. Here, we'll briefly explore their logic, internal structure, and even how to create one with a few lines of code. Please don't convert strings to numbers and use in decision trees. dot file, which is the standard extension for graphviz files. Source(dot_data Build a Decision Tree Classifier. data, breast_cancer. However, we haven't yet put aside a validation set. The tree. Pruning: when you make your tree shorter, for instance because you want to avoid overfitting. Step 4: Evaluating the decision tree classification accuracy. But I’ve already started this bullet points thing, and I really didn’t want to break the pattern. For example, this tree below has a root node that forces you to make a first decision, based on the following question: "Was 'Sex_male'" less than 0. Jun 20, 2022 · How to Interpret the Decision Tree. import numpy as np. For example, if Wifi 1 strength is -60 and Wifi 5 strength is -50, we would predict the phone is located in room 4. Though the Decision Tree classifier is one of the most sophisticated classification algorithms, it may have certain limitations, especially in real-world scenarios. In the example, a person will try to decide if he/she should go to a comedy show or not. We will use a simple dataset for demonstration purposes. Feb 1, 2022 · The “I want to code decision trees with scikit-learn. May 6, 2023 · Here’s an example of how to build a decision tree using the scikit-learn library in Python: In this code, we first load the iris dataset and split it into training and testing sets. Decision trees are constructed from only two elements — nodes and branches. A decision tree trained with default hyperparameters. Decision Tree Pruning removes unwanted nodes from the overfitted Apr 17, 2022 · April 17, 2022. Decision Trees are one of the most popular supervised machine learning algorithms. [ ] from sklearn. See decision tree for more information on the estimator. Understanding the decision tree structure. The advantages and disadvantages of decision trees. target, iris. 5 Jul 12, 2020 · What are Decision Tree models/algorithms in Machine Learning. Pre-pruning can be controlled through several parameters such as the maximum depth of the tree, the minimum number of samples required for a node to keep splitting and the minimum number of instances required for a leaf . Step 1. import pandas as pd. 1- (p²+q²) where p =P (Success) & q=P (Failure) Calculate Gini for May 17, 2024 · A decision tree is a flowchart-like structure used to make decisions or predictions. The function to measure the quality of a split. clf. popleft() yield current_node. The algorithm produces only binary trees, e. May 30, 2022 · And this happens to each decision tree in a random forest model. Understanding Decision Tree Regressors. The first node from the top of a decision tree diagram is the root node. QUEST is proposed by Loh and Shih (1997), and stands for Quick, Unbiased, Efficient, Statistical Tree. It splits data into branches like these till it achieves a threshold value. This tree seems pretty long. Step 1: Import the required libraries. plot_tree without relying on the dot library which is a hard-to-install dependency which we will cover later on in the blog post. 041) We can also use the AdaBoost model as a final model and make predictions for regression. Calculate Gini impurity for sub-nodes, using the formula subtracting the sum of the square of probability for success and failure from one. fit (breast_cancer. Ensembles are constructed from decision tree models. Load the data set using the read_csv () function in pandas. Apr 18, 2024 · Call model. A crucial step in creating a decision tree is to find the best split of the data into two subsets. Jul 27, 2019 · y = pd. dot file will be saved in the same directory as your Jupyter Notebook script. One of them is ID3 (Iterative Dichotomiser 3) and we are going to see how to code it from scratch using ONLY Python to build a Decision Tree Classifier. Jan 22, 2023 · Step 1: Choose a dataset you like or use this example. No matter which decision tree algorithm you are running: ID3, C4. columns) graph = graphviz. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. But this is only one side of the coin; let’s check out the other. Scikit-Learn decision tree implementation is based on CART algorithm. This is to accommodate other datasets in which the class label is the last element on each line (which would be most easily specified by using a -1 value). The decision tree is like a tree with nodes. ” example is a split. The branches depend on a number of factors. from sklearn. Decision tree regressors work by dividing the feature space into regions and assigning a constant value (typically the mean or median) to each region. Read more in the User Guide. Here, we can use default parameters of the DecisionTreeRegressor class. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. label = most common value of Target_attribute in Examples. Before we dive into the code, let’s define the metric used throughout the algorithm. Oct 3, 2020 · Here, we'll extract 10 percent of the samples as test data. Step 3: Training the decision tree model. Next, we'll define the regressor model by using the DecisionTreeRegressor class. 5 and CART. You'll do so using all the 30 features in the dataset, which is split into 80% train and 20% test. Apr 10, 2024 · Decision tree pruning is a technique used to prevent decision trees from overfitting the training data. Hyperparameter Tuning: The Decision Tree model used in this example relies on default hyperparameters. Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. Separate the independent and dependent variables using the slicing method. Jun 3, 2020 · In this exercise, you'll train a classification tree on the Wisconsin Breast Cancer dataset using entropy as an information criterion. In this article, we'll learn about the key characteristics of Decision Trees. tree. 1. Criterion: defines what function will be used to measure the quality of a split. We can see that if the maximum depth of the tree (controlled by the max If the issue persists, it's likely a problem on our side. A python library for decision tree visualization and model interpretation. py') Classifier name (Optional, by default the classifier is the last column of the dataset) Introduction to Decision Trees. append(0) while stack: current_node = stack. The example below demonstrates this on our regression dataset. In this script: We first import the In a decision tree, which resembles a flowchart, an inner node represents a variable (or a feature) of the dataset, a tree branch indicates a decision rule, and every leaf node indicates the outcome of the specific decision. We can split up data based on the attribute Mar 7, 2023 · 4 Python code Examples. This algorithm can be used for regression and classification problems — yet, is mostly used for classification problems. plot_tree() to display the resulting decision tree: model. Decision Tree Classification in Python Tutorial. 3 Wine Quality Dataset. Feb 18, 2023 · CART Decision Tree Python Example. 5 and each decision tree will be fit on a bootstrap sample Mar 18, 2020 · No matter which decision tree algorithm you are running: ID3, C4. The data frame appears as below with the target variable (Reverse). tree import export_text. This is highly misleading. Decision trees are preferred for many applications, mainly due to their high explainability, but also due to the fact that they are relatively simple to set up and train, and the short time it takes to perform a prediction with a decision tree. Nov 7, 2023 · First, we’ll import the libraries required to build a decision tree in Python. 3. DecisionTreeClassifier class from sklearn. Apr 1, 2020 · As of scikit-learn version 21. In this case, the accuracy is 80. It overcomes the shortcomings of a single decision tree in addition to some other advantages. To improve the model’s performance, you can use Jun 18, 2023 · Decision tree algorithm with sample python code. Trees are added one at a time to the ensemble and fit to correct the prediction errors made by prior models. Is a predictive model to go from observation to conclusion. In this tutorial you will discover how you can plot individual decision trees from a trained gradient boosting model using XGBoost in Python. Each node encapsulates information crucial for decision-making within the tree. Don’t forget to include the feature_names parameter, which indicates the feature names, that will be used when displaying the tree. The feature attribute signifies the feature used for splitting, while value stores the specific value of that feature for the split. Moreover, when building each tree, the algorithm uses a random sampling of data points to train Oct 8, 2021 · Performing The decision tree analysis using scikit learn. In this article, we discussed a simple but detailed example of how to construct a decision tree for a classification problem and how it can be used to make predictions. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical Jun 5, 2023 · 1 7 Essential Techniques for Data Preprocessing Using Python: A Guide for Data Scientists 2 From Data to Prediction : Mastering Simple Linear Regression with python 3 more parts 3 Mastering Multiple Linear Regression: A Step-by-Step Implementation Guide with Python Code Examples 4 Polynomial Regression with Python: A Flexible Approach for Non-Linear Curve Fitting 5 Support Vector The output of the code is the accuracy of the decision tree classifier. If it Apr 27, 2021 · 1. Decision trees are an intuitive supervised machine learning algorithm that allows you to classify data with high degrees of accuracy. May 16, 2018 · Sklearn learn decision tree classifier implements only pre-pruning. A: It reduces the possibility of overfitting, as the decision trees are based on subsets Examples concerning the sklearn. We start by importing dataset and necessary dependencies Apr 26, 2021 · The “max_samples” argument can be set to a float between 0 and 1 to control the percentage of the size of the training dataset to make the bootstrap sample used to train each decision tree. Decision trees are useful for a variety of applications, including machine learning and data analysis, due to their intuitive visual depiction. The code uses only NumPy, Pandas and the standard…. The decision attribute for Root ← A. Visualizing decision trees is a tremendous aid when learning how these models work and when Apr 30, 2023 · Now that we have a working example of a Decision Tree model for classification using PySpark MLlib, let’s discuss some further improvements and potential applications of this approach. 2 leaves). e. In [0]: import numpy as np. The topmost node in a decision tree is known as the root node. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for your model, how hetianle / QuestDecisionTree. The from-scratch implementation will take you some time to fully understand, but the intuition behind the algorithm is quite simple. A decision tree is one of the supervised machine learning algorithms. Our training set has 9568 instances, so the maximum value is 9568. The classifier predicts the new data as 1. import pandas as pd . 5 Useful Python Libraries for Decision trees and random forests. It consists of nodes representing decisions or tests on attributes, branches representing the outcome of these decisions, and leaf nodes representing final outcomes or predictions. You can already see why this method results in different decision trees. Decision Tree. We fit the classifier to the data and predict using some new data. The target is to predict whether or not Justice Steven voted to reverse the court decision with 1 means voted to reverse the decision and 0 means he affirmed the decision of the court. from_codes(iris. Dec 7, 2020 · Learn the key concepts of decision trees in Python, such as attribute selection measure, entropy, information gain, and gain ratio. 4. This dataset is made up of 4 features : the petal length, the petal width, the sepal length and the sepal width. They all look for the feature offering the May 15, 2019 · For instance, in AdaBoost, the decision trees have a depth of 1 (i. Apr 18, 2021 · Apr 18, 2021. 5, CART, CHAID or Regression Trees. Jan 7, 2021 · Decision Tree Code in Python. A decision tree consists of the root nodes, children nodes Dec 24, 2019 · We export our fitted decision tree as a . X. Jan 3, 2018 · Let's first decide what training set sizes we want to use for generating the learning curves. Conclusion. For each decision tree, a new dataset is formed out of the original dataset. A Decision Tree is a Flow Chart, and can help you make decisions based on previous experience. Post pruning decision trees with cost complexity pruning. Apr 19, 2020 · Step #3: Create the Decision Tree and Visualize it! Within your version of Python, copy and run the below code to plot the decision tree. Oct 26, 2020 · Python for Decision Tree. 10) Training the model. Step 2: Find Likelihood probability with each attribute for each class. predict (X_test) 5. May 8, 2022 · A big decision tree in Zimbabwe. The following is Python code Oct 13, 2023 · To create our tree from scratch first we create a class called DecisionTree in python. Algorithm. Step 2. Step 2: Initialize and print the Dataset. Each internal node corresponds to a test on an attribute, each branch Nov 25, 2023 · In this post, the bagging classifier is created using Sklearn BaggingClassifier with a number of estimators set to 100, max_features set to 10, and max_samples set to 100 and the sampling technique used is the default (bagging). Here, you should watch the following video to understand how decision tree algorithms work. Let’s use a relevant example: the Iris dataset, a Boosting algorithms combine multiple low accuracy (or weak) models to create a high accuracy (or strong) models. It structures decisions based on input data, making it suitable for both classification and regression tasks. Decision trees are a non-parametric model used for both regression and classification tasks. Decision Tree Regression. 0 (roughly May 2019), Decision Trees can now be plotted with matplotlib using scikit-learn’s tree. The code below plots a decision tree using scikit-learn. The target variable to predict is the iris species. Update Mar/2018: Added alternate link to download the dataset as the original appears […] Once you've fit your model, you just need two lines of code. Figure 17. For example, in the Cholesterol attribute, values showing ‘LOW’ are processed to 0 and ‘HIGH’ to be 1. 8022471910112359 **Conclusion** This is an in-depth solution for decision tree in Google Colab in Python with proper code examples and outputs. In this post we’re going to discuss a commonly used machine learning model called decision tree. If the issue persists, it's likely a problem on our side. Parameters: criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. The options are “gini” and “entropy”. Oct 27, 2021 · Limitations of Decision Tree Algorithm. setosa=0, versicolor=1, virginica=2 Building a Simple Decision Tree. feature 3 and feature 1; and so on… 3 Advantages and 3 disadvantages of decision trees in your project. plot_tree(clf) This plots the following tree: Feb 9, 2023 · Implement Decision Tree Classification in Python. As a result, it learns local linear regressions approximating the circle. Jan 22, 2022 · Jan 22, 2022. accuracy = 0. Iris species. We then Feb 26, 2021 · A decision tree is a flowchart-like tree structure where an internal node represents feature (or attribute), the branch represents a decision rule, and each leaf node represents the outcome. First, import export_text: from sklearn. In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. Dec 5, 2022 · Decision Trees represent one of the most popular machine learning algorithms. And other tips. target_names) In the proceeding section, we’ll attempt to build a decision tree classifier to determine the kind of flower given its dimensions. If the model has target variable that can take a discrete set of values, is a classification tree. There can be instances when a decision tree may perform better than a random forest. Then, they add a decision rule for the found feature and build an another decision tree for the sub data set recursively until they reached a decision. py accepts parameters passed via the command line. Some of its deterrents are as mentioned below: Decision Tree Classifiers often tend to overfit the training data. target) Decision Tree Analysis is a general, predictive modelling tool that has applications spanning a number of different areas. pyplot as plt. Mar 2, 2019 · To demystify Decision Trees, we will use the famous iris dataset. First, the AdaBoost ensemble is fit on all available data, then the predict () function can be called to make predictions on new data. Let’s start from the root: The first line “petal width (cm) <= 0. metrics import r2_score. Jun 8, 2023 · In this blog post, we’ll walk through a step-by-step guide on how to implement decision trees in Python using the scikit-learn library. They all look for the feature offering the highest information gain. Jan 2, 2024 · The provided Python code defines a class called Node for constructing nodes in a decision tree. II/II. fit (X_train,y_train) #Predict the response for test dataset. clf = DecisionTreeClassifier (max_depth=3) #max_depth is maximum number of levels in the tree. Step 5: (sort of optional) Optimizing the Apr 26, 2020 · Running the example fits the Bagging ensemble model on the entire dataset and is then used to make a prediction on a new row of data, as we might when using the model in an application. If Examples vi , is empty. The recursive create_decision_tree() function below uses an optional parameter, class_index, which defaults to 0. To make the rules look more readable, use the feature_names argument and pass a list of your feature names. There are three of them : iris setosa, iris versicolor and iris virginica. Here’s some code on how you can run a decision tree in Python using the sklearn library for machine learning: ## Dependencies. Categorical. To train our tree we will develop a “train” function and after training to predict an output we will Jun 4, 2021 · We start by importing the tree module from scikit-learn and initializing the dummy data and the classifier. , non-leaf nodes always have two children. 2: Splitting the dataset. There are other algorithms such as ID3 which can produce decision trees with nodes that have more than two children. How to create a predictive decision tree model in Python scikit-learn with an example. The possible paramters are: Filename for training (Required, must be the first argument after 'python decision-tree. It learns to partition on the basis of the attribute value. The minimum value is 1. 6 Datasets useful for Decision trees and random forests. Predicted Class: 1. Explore and run machine learning code with Kaggle Notebooks | Using data from PlayTennis. def breadth_first_traversal(tree): stack = deque() stack. Multi-output Decision Tree Regression. Unexpected token < in JSON at position 4. Dec 13, 2020 · In that article, I mentioned that there are many algorithms that can be used to build a Decision Tree. export_graphviz(dtree, out_file=None, feature_names=X. Including splitting (impurity, information gain), stop condition, and pruning. The aim of this article is to make all the parts of a decision tree classifier clear by walking through the code that implements the algorithm. plot_tree() In Colab, you can use the mouse to display details about specific elements such as the class distribution in each node. Decision tree is one of most basic machine learning algorithm which has wide array of use cases which is easy to interpret & implement. 2%. Step 2: Prepare the dataset. 2. tree in Python. Now that we are familiar with using Bagging for classification, let’s look at the API for regression. Steps to Calculate Gini impurity for a split. Refresh the page, check Medium ’s site status, or find something interesting to read. tree import DecisionTreeClassifier. Image by author. In this chapter we will show you how to make a "Decision Tree". 6. gini: we will talk about this in another tutorial. Max_depth: defines the maximum depth of the tree. Nov 22, 2021 · Example: Predicting Judge Stevens Decision. Decision Tree Classifier is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In decision tree classifier, the A Decision Tree is a supervised Machine learning algorithm. Plot the decision surface of decision trees trained on the iris dataset. We won’t look into the codes, but rather try and interpret the output using DecisionTreeClassifier() from sklearn. Jul 12, 2021 · This is article number two in a series dedicated to Tree Based Algorithms, a group of widely used Supervised Machine Learning Algorithms. You can use this code as a starting point to build your own decision tree Jul 18, 2020 · This is a classic example of a multi-class classification problem. For each possible value, vi, of A, Add a new tree branch below Root, corresponding to the test A = vi. To maximize the potential of decision trees, you must first comprehend their core components. In addition, the predictions made by each decision tree have varying impact on the final prediction made by the model. (2020). Jun 22, 2022 · CART (Classification and Regression Tree) uses the Gini method to create binary splits. Mar 23, 2018 · Below is a snippet of the decision tree as it is pretty huge. On SciKit - Decission Tree we can see the only way to do so is by min_impurity_decrease but I am not sure how it specifically works. Jul 14, 2020 · Decision Tree Classification algorithm. Stay tuned! Aug 27, 2018 · We will mention a step by step CART decision tree example by hand from scratch. . Wizard of Oz (1939) Vlog. Aug 27, 2020 · Plotting individual decision trees can provide insight into the gradient boosting process for a given dataset. It is a tree-structured classification algorithm that yields a binary decision tree. In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package. g. 327 (4. This article delves into the components, terminologies, construction, and advantages of decision trees, exploring their Apr 26, 2021 · Gradient boosting is also known as gradient tree boosting, stochastic gradient boosting (an extension), and gradient boosting machines, or GBM for short. Python3. Explore and run machine learning code with Kaggle Notebooks | Using data from Heart Disease Prediction. See how to use scikit-learn library to build a decision tree classifier for the iris dataset. The next, and last article in this series, explores Gradient Boosted Decision Trees. An example to illustrate multi-output regression with decision tree. import matplotlib. Let’s plot using the built-in plot_tree in the tree module. clf = DecisionTreeClassifier () # Train Decision Tree Classifier. Reference of the code Snippets below: Das, A. Refresh. # Create Decision Tree classifier object. It is used in both classification and regression algorithms. head() Although, decision trees can handle categorical data, we still encode the targets in terms of digits (i. Boosting algorithms such as AdaBoost, Gradient Boosting, and XGBoost are widely used machine learning algorithm to win the data science competitions. The method applied is random patches as both the samples and features are drawn in a random manner. I prefer Jupyter Lab due to its interactive features. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for Jul 17, 2021 · A Random Forest is a powerful ensemble model built with large number of Decision Trees. keyboard_arrow_up. from sklearn import tree. 8” is the decision rule applied to the node. [online] Medium. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. The random forest is a machine learning classification algorithm that consists of numerous decision trees. In Python, the scikit-learn module provides a simple interface for implementing decision trees. Pruning aims to simplify the decision tree by removing parts of it that do not provide significant predictive power, thus improving its ability to generalize to new data. Let’s change a couple of parameters to see if there is any effect on the accuracy and also to make the tree shorter. clf = clf. There is no way to handle categorical data in scikit-learn. (Okay, you’ve caught me red-handed, because this one is not in the image. ## Data: student scores in (math, language, creativity) --> study field. A decision tree classifier. A comparison study of QUEST and other algorithms was conducted by Lim et al (2000). MAE: -72. Each decision tree in the random forest contains a random sampling of features from the data set. 2 Breast Cancer Wisconsin (Diagnostic) Dataset. Gini impurity. Jan 6, 2023 · Fig: A Complicated Decision Tree. For actual use, I suggest you turn this into a generator: from collections import deque. plot_tree(clf); Mar 15, 2024 · A decision tree in machine learning is a versatile, interpretable algorithm used for predictive modelling. Step 3: Put these value in Bayes Formula and calculate posterior probability. What is a decision tree classifier? It is a tree that allows you to classify data points, which are also known as target variables, based on feature variables. y_pred = clf. Everything explained with real-life examples and some Python code. Note that the new node on the left-hand side represents samples meeting the deicion rule from the parent node. Create notebooks and keep track of their status here. I would like to walk you through a simple example along with the python code. 1 Decision Trees. Feb 5, 2020 · Decision Tree. Then below this new branch add a leaf node with. content_copy. How to make the tree stop growing when the lowest value in a node is under 5. import numpy as np . Predictions are performed by traversing the tree from root to leaf and going left when the condition is true. model_selection import GridSearchCV. 7 Important Concepts in Decision Trees and Random Forests. Display the top five rows from the data set using the head () function. All the code can be found in a public repository that I have attached below: Explore and run machine learning code with Kaggle Notebooks | Using data from Car Evaluation Data Set Since we remove elements from the left and add them to the right, this should represent a breadth-first traversal. After making sure you have dtree, which means that the above code runs well, you add the below code to visualize decision tree: Remember to install graphviz first: pip install graphviz import graphviz from graphviz import Source dot_data = tree. Follow the code to produce a beautiful tree diagram Aug 23, 2023 · 2. It can be utilized in various domains such as credit, insurance, marketing, and sales. tree. SyntaxError: Unexpected token < in JSON at position 4. 1: Addressing Categorical Data Features with One Hot Encoding. But that does not mean that it is always better than a decision tree. The decision trees is used to predict simultaneously the noisy x and y observations of a circle given a single underlying feature. Decision Tree Classifier and Cost Computation Pruning using Python. Bootstrapping: Randomizing the input data. In this example, we will use the social network ads data concerning the Gender, Age, and Estimated Salary of several users and based on these data Jan 11, 2023 · Here, continuous values are predicted with the help of a decision tree regression model. tree module. How the popular CART algorithm works, step-by-step. 1 Iris Dataset. Here is the code to produce the decision tree. decision-tree. rr mg jo xm as ba ha aq th gy