Ernest Chan. A, B, and C - A Decision Tree can be used for both classification and regression problems. Oct 29, 2017 · Gini Impurity (With Examples) 2 minute read TIL about Gini Impurity: another metric that is used when training decision trees. tree 🌲xiixijxixij. 2. Can. The Gini index, entropy, and misclassification rate are three commonly used impurity measures in decision tree algorithms. One of the most popular methods for assessing the quality of a split is called “Gini Impurity”. Mar 30, 2020 · More formally, the decision tree is the algorithm that partitions the observations into similar data points based on their features. The Gini index is the most widely used cost function in decision trees. g. In simple terms, Gini impurity is the measure of impurity in a node. 10. Mar 15, 2024 · In decision trees, entropy is a measure of impurity or disorder within a dataset. Question: We would like to build a decision tree from the… impurity, such as a peptide- or protein-related impurity. If we set min_impurity_decrease, then the model will use this value as a threshold before splitting. Understanding the role of leaf nodes is essential for effectively Sep 10, 2014 · "Gini impurity" mainly used in Decision Tree learning, measures the impurity of a categorical variable, such as colour, sex, etc. Thus, although these traditional methods are accurate in practice It continues the process until it reaches the leaf node of the tree. tree import DecisionTreeClassifier. Because Gini impurity is used to train the decision tree itself, it is computationally inexpensive to calculate. It sets a threshold on gini. However, as the tree size grows the model interpretability deteriorates. Entropy, Gini index. When all observations belong to the same label Feb 24, 2019 · 지니 불순도 측정(Gini Impurity Measure)은 Classification Problem에서 사용 가능한 결정 트리(Decision Tree)의 분할 기준 (Split Criteria) 중 하나이다. The conclusion, such as a class label for classification or a numerical value for regression, is represented by each leaf node in the tree-like structure that is constructed, with each internal node representing a judgment or test on a feature. gold). For instance, if min_impurity_split is set to 0. Jul 19, 2019 · Now, let's determine the quality of each split by weighting the impurity of each branch. It is one of the most widely used and practical methods for supervised learning. nd better measures of impurity than misclassi cation rate. 2 Refer to ICH Guideline on Impurities in New Drug Substances Definition: upper confidence limit = three times the standard deviation of batch analysis data YES YES NO NO Nov 6, 2020 · Classification. Gini impurity is the probability of incorrectly classifying a random data point in a dataset. Here, X is the feature attribute and y is the target attribute (ones we want to predict). The decision for each of the region would be the majority class on it. Impurity Measures . Gini impurity is a function that determines how well a decision tree was split. Dec 6, 2022 · Gini impurity. tree_ also stores the entire binary tree structure, represented as a Apr 10, 2024 · Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs, and utility. We will A decision tree classifies data items ( Fig. They can can be used either to drive informal discussion or to map out an algorithm that predicts the best choice mathematically. Schedule 1:1 free counselling Talk to Career Expert. . The questions thereby form a hierarchy, encoded as a tree. Each node shows (1) the predicted class, (2) the predicted probability of NEG and (3) the percentage of observations in the node. Sep 15, 2022 · Classification Tree คือ Decision Tree ที่ใช้สำหรับการทำ Classification โดยจะใช้ Gini Impurity หรือ Entropy เป็น Objective Function ในการหาจุดที่ดีที่สุดในการแบ่งข้อมูล (Split point) Jun 7, 2019 · Information Gain, like Gini Impurity, is a metric used to train Decision Trees. 11. Blindly using information gain can be problematic. Decision Trees #. Viewed 679 times 0 $\begingroup$ I have recently stepped DECISION TREE #1: ESTABLISHING ACCEPTANCE CRITERION FOR A SPECIFIED IMPURITY IN A NEW DRUG SUBSTANCE 1 Relevant batches are those from development, pilot and scale-up studies. The space is split using a set of conditions, and the resulting structure is the tree“ How can I get the total weighted Gini impurity (or entropy) on a trained decision tree in scikit-learn? For instance, the following code on the titanic dataset, import pandas as pd import matplotlib. So all below mentioned measures differ in formula but align in goal. Split at 6. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Other approaches, by (C), might give short Jun 23, 2016 · What is node impurity/purity in decision trees? Classification Trees. According to Wikipedia, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset. right-column[ ] Here is an example of a tree with depth one, that’s basically just thresholding a single feature. It is the most popular and the easiest way to split a decision tree and it works only with categorical targets as it only does binary splits. plot () function. A decision tree is a map of the possible outcomes of a series of related choices. It derives its name from the Italian mathematician Corrado Gini. 3, a node needs to have a gini value that is more then 0. Step-2: Find the best attribute in the dataset using Attribute Selection Measure (ASM). Entropy measures often work long-term by (A) and (B), and if something goes wrong it's easier to track down and explain why (e. 5 and CART, rely on impurity-reduction functions that promote the discriminative power of each split. The Gini Index is straightforward and carry out We at iNeuron are happy to announce multiple series of courses. Feb 24, 2023 · Gini Impurity: The internal working of Gini impurity is also somewhat similar to the working of entropy in the Decision Tree. One commonly used measure of impurity is Gini In short, the cost function of a decision tree seeks to find those cuts that minimize impurity. 3648–0. 3. Attributes that are unique identi ers for rows produces maximum information gain, with little Nov 24, 2020 · Problem solver extraordinaire applying critical thinking through data science. Decision-tree algorithm falls under the category of supervised learning algorithms. t. plot::rpart. However, depending on prevalence of classes and quirks in the data, it’s usually not as straight forward as it sounds. Splitting Criteria. The two types are commonly referred to together at CART (Classification and Regression Tree). However, there is no reason why a tree should be symmetrical. Traditional tree-induction algorithms, such as C4. The decision tree can be used for classification or regression Feb 17, 2020 · Minimize impurity ] . Initializing the X and Y parameters and loading our dataset: iris = load_iris() X = iris. Finally we are covering Big Data,Cloud,AWS,AIops and MLops. A decision tree where the target variable takes a continuous value, usually numbers, are called Regression Trees. Feature 1: Balance. Oct 28, 2017 · It is sometimes called “gini importance” or “mean decrease impurity” and is defined as the total decrease in node impurity (weighted by the probability of reaching that node (which is Jun 24, 2024 · In a decision tree, the Gini Index is a measure of node impurity that quantifies the probability of misclassification; it helps to determine the optimal split by favoring nodes with lower impurity (closer to 0), indicating more homogeneous class distributions. It is an impurity metric since it shows how the model differs from a pure division. criterion: string, optional (default=”gini”): The function to measure the quality of a split. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes. Splitting the Dataset: The dataset is split into subsets based on the selected attribute. A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. Classifying tumors and spam mail classification are examples of classification problems since the target variable is a discrete value while stock price prediction is a regression problem since the target variable is a continuous value. It was proposed by Leo Breiman in 1984 as an impurity measure for decision tree learning and is given by the equation/formula; where P=(p 1, p 2 ,. a bug with obtaining the training data). Gini Index or Gini Impurity. Impurity seems like it should be a simple calculation. This is usually called the parent node. Mar 26, 2024 · Gain a deep understanding of decision trees, including theoretical foundations such as standard deviation reduction, information gain, Gini impurity, and chi-square. compute_node_depths() method computes the depth of each node in the tree. Categorical Variable Decision Tree (Classification Tree) Merupakan algoritma Decision Tree yang khusus menangani/memprediksi dataset yang variabel target nya berupa data kategorik (categorical data). In low dimensions it is actually quite powerful: It can learn non-linear decision boundaries and naturally can handle multi-class problems. One hyper-parameter that seems to get much less attention is min_impurity_decrease. It is one of the methods of selecting the best splitter; another famous method is Entropy which ranges from 0 to 1. Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. 5 (very impure classification) and a minimum of 0 (pure classification). Decision Tree Analysis is a general, predictive modelling tool that has applications spanning a number of different areas. Check out the syllabus below. A node will be split if this split induces a decrease of the impurity greater than or equal to this value. However, Gini impurity is somewhat biased toward selecting numerical features (rather than categorical features). If None then unlimited number of leaf nodes. Pruning and setting appropriate stopping criteria are used to address this assumption. Nov 28, 2023 · from sklearn. Tree structure: CART builds a tree-like structure consisting of nodes and branches. , Entropy is a measure of disorder or impurity in the given dataset. We can avoid Jul 29, 2017 · In these trees, each node, or leaf, represent class labels while the branches represent conjunctions of features leading to class labels. In the Decision Tree algorithm, both are used for building the tree by splitting as per the appropriate features but there is quite a difference in the computation of both methods. Basically, it helps us to determine which splitter is best so that we can build a pure decision tree. May 31, 2024 · The decision tree algorithm learns that it creates the tree from the dataset via the optimization of the cost function. com/channel/UCG04dVOTmbRYPY1wvshBVDQ/join. The Gini Index is a significant impurity measure utilized in decision tree algorithms for classification tasks. Initializing a decision tree classifier with max_depth=2 and fitting our feature Jul 22, 2020 · This video will help you to understand about basic intuition of Entropy, Information Gain & Gini Impurity used for building Decision Tree algorithm. The term “impurity” in this context reflects the inclusion of multiple classes within a subset. 1a) by posing a series of questions about the features associated with the items. The bra Apr 25, 2021 · Decision Tree is a simple machine learning algorithm, which can do both classification and regression technique on the dataset. Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to Create decision tree. In this example, the question being asked is, is X1 less than or equal to 0. It quantifies the uncertainty associated with classifying instances, guiding the algorithm to make informative splits for effective decision-making. Apr 17, 2019 · In the case of Classification Trees, CART algorithm uses a metric called Gini Impurity to create decision points for classification tasks. On each step or node of a decision tree, used for classification, we try to form a condition on the features to separate all the labels or classes contained in the dataset to the fullest purity. It measures the probability of the tree to be wrong by sampling a class randomly using a distribution from this node: Ig(p) = 1 − ∑i=1J p2 i I g ( p) = 1 − ∑ i = 1 J p i 2. Gini Impurity of features after The impurity function can be defined in different ways, but the bottom line is that it satisfies three properties. 5, which indicates the likelihood of new, random data being miss classified if it were given a random class label according to the Dec 25, 2023 · A decision tree is a non-parametric model in the sense that we do not assume any parametric form for the class densities, and the tree structure is not fixed a priori, but the tree grows, branches and leaves are added, during learning depending on the complexity of the problem inherent in the data. Motivation for Decision Trees. In the case of classification problems, the cost or the loss function is a measure of impurity in the target column of nodes belonging to a root node. DECISION TREE! IN A NUTSHELL… Sep 15, 2021 · Limit the maximum number of leaves in a tree. This is 0. Now, let’s see what ways exist to calculate impurity: Calculate impurity using the Gini index. It works for both continuous as well as categorical output variables. Is. This seems to be the same as misclassification. p n) , and p i is the probability of an object that is being classified to a particular class. • An impurity could be a surrogate for other impurities that might be clinically relevant or for which there is increased uncertainty. The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first. The Gini Impurity concept is very related to the entropy one. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Homogeneity means that most of the samples at each node are from one class. Dec 2, 2020 · Decision Trees are one of the best known supervised classification methods. In terms of data analytics, it is a type of algorithm that includes conditional ‘control’ statements to classify data. 0901 (the same as the code!) I said earlier you can ask decision trees what features in the data are the most important and you would do this by adding up the reduction in purity for Apr 29, 2021 · Impurity measures are used in Decision Trees just like squared loss function in linear regression. Tree models where the target variable can take a discrete set of values are called Decision Trees are often used to answer that kind of question: Given a labelled dataset, how should we classify new samples? Labelled: Our dataset is labelled because each point has a class (color): blue or green. From the analysis perspective the first node is the root node, which is the first variable that splits the target variable. Specifically, these metrics measure the quality of a split . If we have 80% of class C1 and 20% of class C2, labelling t. target. The max_depth hyperparameter controls the overall complexity of the tree. 4 minute read. Sep 20, 2023 · Leaf nodes are the final decision-makers in decision trees, determining the class labels or regression values assigned to input data points. A tree can be seen as a piecewise constant approximation. It measures the probability of a haphazardly picked test being misclassified by a decision tree algorithm, and its value goes from 0 (perfectly pure) to 1 (perfectly impure). There are however a few catches: kNN uses a lot of storage (as we are required to store the entire training data), the more Decision trees are popular classification models, providing high accuracy and intuitive explanations. min_impurity_decrease float, default = 0. A few prerequisites: please read this and this article to understand the basics of predictive analytics and machine learning. Indeed, optimal generalization performance could be reached by growing some of the Dec 11, 2020 · The Gini impurity measure is one of the methods used in decision tree algorithms to decide the optimal split from a root node, and subsequent splits. More precisely, the Gini Impurity of a data set is a number between 0-0. The logarithm is base 2. This value - Gini Gain is used to picking the best split in a decision tree. They play a critical role in minimizing impurity, influencing tree depth and complexity, and enhancing the interpretability of the model. The complete process can be better understood using the below algorithm: Step-1: Begin the tree with the root node, says S, which contains the complete dataset. The tree_. Sounds similar right? "inequality" and "impurity" are both measures of variation, which are intuitively the same concept. , including the hyper-parameters that are only for random forests as well. As you can see from the diagram below, a decision tree starts with a root node, which does not have any Jan 11, 2019 · I’m going to show you how a decision tree algorithm would decide what attribute to split on first and what feature provides more information, or reduces more uncertainty about our target variable out of the two using the concepts of Entropy and Information Gain. Nov 2, 2022 · Flow of a Decision Tree. data[:, 2 :] y =iris. We would like to show you a description here but the site won’t allow us. If you Grow a tree with max_leaf_nodes in best-first fashion. Total impurity of leaves vs effective alphas of pruned tree# Minimal cost complexity pruning recursively finds the node with the “weakest link”. The splitting criteria used by the regression tree and the classification tree are different. Develop practical proficiency in implementing decision tree models using Python and scikit-learn, with step-by-step guidance and code explanations. A decision tree starts at a single point (or ‘node’) which then branches (or ‘splits’) in two or more directions. Unlike Entropy, Gini impurity has a maximum value of 0. Classify: To classify a new datapoint is to assign a class (color) to it. You will learn more May 11, 2018 · This goal of this model was to explain how Scikit-Learn and Spark implement Decision Trees and calculate Feature Importance values. In this simple example, only one feature remains, and we can build the final decision tree. Decision Trees - RDD-based API. Mar 23, 2024 · These real-world applications underscore the significance of the Gini index in decision tree algorithms and its pivotal role in driving actionable insights and informed decision-making. The decision classifier has an attribute called tree_ which allows access to low level attributes such as node_count, the total number of nodes, and max_depth, the maximal depth of the tree. Its formula is: The reduction in impurity is the starting group Gini impurity minus the weighted sum of impurities from the resulting split groups. Gini index is used in most decision tree libraries. Show transcribed image text. It also does not take into account the correlation between features. 2747 = 0. Wie bei den k-Nearest-Neighbor und Support Vector Machines auch, möchten wir hier nur eine Intuition aufzeigen, wie Decision Trees Jan 2, 2020 · Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented as sets of if-else/then rules to improve human readability. 1. A decision tree is a graphical representation of all possible solutions to a decision based on certain conditions. When deciding which measures to use in machine learning it often comes down to long-term vs short-term gains, and maintainability. We’ll need a higher depth to get a good Decision Tree. Sep 16, 2022 · Indeed the Decision Tree gives priority to the classes with the highest number of wines. which is a classification problem -- getting the "majority" of each group. Question: What is the final objective of Decision Tree? Maximise the Gini Index of the leaf nodes Minimise the homogeneity of the leaf nodes Maximise the heterogeneity of the leaf nodes Minimise the impurity of the leaf nodes. The nodes represent different decision the impurity measure. It allows an individual or organization to weigh possible actions against one another based on their costs, probabilities, and benefits. 5: Sep 8, 2019 · During my time learning about decision trees and random forests, I have noticed that a lot of the hyper-parameters are widely discussed and used. Example: Given that Prob (Bus) = 0. 5, which indicates the likelihood of new, random data being misclassified if it were given a random class label according to the class In a decision tree, Gini Impurity [1] is a metric to estimate how much a node contains different classes. It is one way to display an algorithm that only contains conditional control statements. A flexible and comprehensible machine learning approach for classification and regression applications is the decision tree. The Decision Tree then makes a sequence of splits based in hierarchical order of impact on this target variable. Gini Impurity Index; Entropy; The most popular and efficient way Nov 11, 2019 · Since the decision tree is primarily a classification model, we will be looking into the decision tree classifier. In layman terms, Gini Gain = original Gini impurity - weighted Gini impurities So, higher the Gini Gain is better the split. 첫째, 지니 불순도 측정치가 결정 트리에서 사용되는 방법과는 독립적으로 다양한 각도에서 동기를 부여하여 지니 불순도 2. Intuitively, you can think of a set of examples as the set of atoms in a metallic ball, while the class of an example is like the kind of an atom (e. Comparison with Other Impurity Measures. Jul 28, 2020 · Min_impurity_split parameter can be used to control the tree based on impurity values. 3 and Prob (Train) = 0. Apr 17, 2023 · In its simplest form, a decision tree is a type of flowchart that shows a clear pathway to a decision. Feel free to check out that post first before continuing. Impurity is presence of more than one class in a subset of data. A decision tree is a decision support hierarchical model that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. The decision tree is a supervised learning model that has the tree-like structured, that is, it contains the root, parent/children nodes, and leaves. Pruned Decision Tree. 0. 3 to be further splitted. We try to arrive at as lowest impurity as possible by the algorithm of our choice. One way to measure impurity degree is using entropy. 3, we can now compute entropy as. Like the regression tree, the goal of the classification tree is to divide the data into smaller, more homogeneous groups. Determine impurity level in relevant batches1. This is the default tree plot made bij the rpart. 0596. Max_depth, min_samples_leaf etc. 5. In this video, I explained what is meant by Entropy, Information Gain, Jan 31, 2021 · Decision Trees einfach erklärt. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. Jan 1, 2023 · The Gini Impurity is the weighted mean of both: Case 2: Dataset 1: Dataset 2: The Gini Impurity is the weighted mean of both: That is, the first case has lower Gini Impurity and is the chosen split. Entry 48: Decision Tree Impurity Measures. It is a supervised learning algorithm that learns from labelled data to predict unseen data. youtube. Jul 15, 2024 · Classification and Regression Trees (CART) is a decision tree algorithm that is used for both classification and regression tasks. e. Hopefully by reaching the end of this post you have a better understanding of the appropriate decision tree algorithms and impurity criterion, as well as the formulas used to determine the importance of each Jan 15, 2022 · Check membership Perks: https://www. Gini impurity ranges values from 0 to 0. Also, an attribute/feature with least gini index is preferred as root node while making a decision tree. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. Sep 29, 2019 · 1. A node is pure if all samples in its dataset belong to the same class and is most impure when an equal number of samples belong to each class. DecisionTreeClassifier. Nov 7, 2022 · Decision Tree dibagi menjadi 2 jenis berdasarkan dari jenis target class (dependent variable) pada dataset, yaitu : 1. May 22, 2024 · Understanding Decision Trees. Feb 25, 2021 · Gini Impurity is a measurement used to build Decision Trees to determine how the features of a data set should split nodes to form the tree. The boundary between the 2 regions is the decision boundary. I am reading the gini index definition for decision tree: Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset. May 17, 2024 · How Decision Trees Work? The process of creating a decision tree involves: Selecting the Best Attribute: Using a metric like Gini impurity, entropy, or information gain, the best attribute to split the data is selected. For example, say we have the following data: May 31, 2024 · Decision trees are prone to overfitting when they capture noise in the data. Therefore, in order to build a good decision tree algorithm, we’ll need a method for evaluating splits. Entropy of a pure table (consist of single class) is zero because the probability is 1 and log (1) = 0. Gini Impurity gives an idea of how fine a split is (a measure of a node’s “purity”), by how mixed the classes are in the two groups created by the split. Aug 26, 2021 · Decision tree algorithm is used in many applications such as medical production, manufacturing, financial analysis, etc. 4, Prob (Car) = 0. 0 When evaluating every split alternative, how much impurity is reduced is calculated and the most contributing is selected for splitting. This metric measures the impurity of a dataset. Apr 22, 2020 · Impurity measures in decision trees. Summary. There are 3 steps to solve this one. Each question is contained in a node, and every internal node points to one child node for each possible answer to its question. Let us return to the k-nearest neighbor classifier. Definition: An impurity function is a function Φ defined on the set of all K -tuples of numbers ( p 1, ⋯, p K) satisfying p j ≥ 0, j = 1, ⋯, K, Σ j p j = 1 with the properties: Φ achieves maximum only for the uniform Dec 19, 2021 · Gini Impurity, like Information Gain and Entropy, is just a metric used by Decision Tree Algorithms to measure the quality of a split. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Pruning is a technique used to reduce the complexity of a Decision Tree. Plot the decision tree using rpart. You can learn all about decreasing the impurities going down the decision tree model with our course on Decision Trees offered by Dr. Feb 16, 2022 · Not only that, but in this article, you’ll also learn about Gini Impurity, a method that helps identify the most effective classification routes in a decision tree. min_impurity_decrease float, default=0. The Gini Index, also known as Gini Impurity, assists the CART algorithm in identifying the most suitable feature for node splitting during the construction of a decision tree classifier. Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. As explained in previous posts, “A decision tree is a way of representing knowledge obtained in the inductive learning process. Essentially, information gain calculates the di erence between the impurity of the parent node and the impurity of the child nodes. Gini Impurity. Modified 4 years, 1 month ago. Best nodes are defined as relative reduction in impurity. 30 . Gini Impurity is a method that measures the impurity of a dataset. Provost, Foster; Fawcett, Tom. Aug 16, 2023 · A step-by-step explanation of how decision trees handle missing values: Step 1: Attribute Splitting When building a decision tree, the algorithm starts by selecting the attribute that best separates the data based on a chosen criterion, such as Gini impurity or entropy. Expert-verified. In Other hyperparameters in decision trees #. Last week I learned about Entropy and Information Gain which is also used when training decision trees. Nov 8, 2020 · A successful decision tree is one that does a good job of “splitting” data into homogeneous groups. This parameter is adequate under the assumption that a tree is built symmetrically. A decision tree begins with the target variable. Der Decision Tree, auf Deutsch Entscheidungsbaum, ist der dritte Machine Learning Algorithmus, den wir betrachten, bevor wir uns den künstlichen neuronalen Netzen widmen. Ask Question Asked 4 years, 2 months ago. Gini Impurity is calculated using the formula, Gini Impurity is a measurement used to build Decision Trees to determine how the features of a dataset should split nodes to form the tree. Decision trees use impurity measures such as Gini impurity or entropy to evaluate how well a split separates classes. DECISION TREE #1: ESTABLISHING ACCEPTANCE CRITERION FOR A SPECIFIED IMPURITY IN A NEW DRUG SUBSTANCE. 2. More precisely, the Gini Impurity of a dataset is a number between 0-0. The more impure the dataset, the higher is Gini index. NO. Non linear impurity function works better in practice. Here, the tree has not yet had time to analyze the classes containing the least number of wines. Determine mean + upper confidence limit for the impurity (Let this = A) Acceptance criterion = A or B (as appropriate) Is impurity also YES a degradation product? NO. Nov 24, 2022 · Gini Index aims to decrease the impurities from the root nodes (at the top of decision tree) to the leaf nodes (vertical branches down the decision tree) of a decision tree model. yj mg yo se dm wc xe ze hc xl