Decision tree python code example. py accepts parameters passed via the command line.

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