Decision tree examples with solutions machine learning. data[removed]) # assign removed data as input.
CS16: Introduction to Data Structures & Algorithms Summer 2021. We have to convert the non numerical columns 'Nationality' and 'Go' into numerical values. Decision Tree Learning Decision Tree Learning Problem Setting: • Set of possible instances X – each instance x in X is a feature vector x = < x 1, x 2 … x n> • Unknown target function f : X Y – Y is discrete valued • Set of function hypotheses H={ h | h : X Y } – each hypothesis h is a decision tree Input: To draw a decision tree, first pick a medium. Start with the Big value of outlook. Solution: Training can be posed as an optimization problem, in Feb 4, 2023 · Disadvantages of Decision Trees. clf=clf. Sep 7, 2017 · Decision Trees for Classification: A Machine Learning Algorithm. 1. Option 3: replace that part of the tree with one of its subtrees, corresponding to the most common branch in the split. Split the training set into subsets. Oct 25, 2020 · 1. For each possible value, vi, of A, Add a new tree branch below Root, corresponding to the test A = vi. Feb 24, 2023 · Difference between Gini Index and Entropy. The set of visited nodes is called the inference path. Objective: infer class labels; Able to caputre non-linear relationships between features and labels; Don't require feature scaling(e. It can take three values: Big, Medium, and Small. It then splits the data into training and test sets using train Jan 31, 2020 · Introduction From classrooms to corporate, one of the first lessons in machine learning involves decision trees. It influences how a decision tree forms its boundaries. Then below this new branch add a leaf node with. The range of the Gini index is [0, 1], where 0 indicates perfect purity and 1 indicates maximum impurity. While building the decision tree, we would prefer to choose the attribute/feature with the least Gini Index as the root node. RULE 3 If Nov 24, 2022 · The formula of the Gini Index is as follows: Gini = 1 − n ∑ i=1(pi)2 G i n i = 1 − ∑ i = 1 n ( p i) 2. Each internal node is a question on features. Introduction. Each leaf node has a class label, determined by majority vote of training examples reaching that leaf. age. Decision Tree Solved Numerical Example Big Data Analytics ML CART Algorithm by Mahesh Huddar. student. Draw a small box to represent this point, then draw a line from the box to the right for each possible solution or action. where, ‘pi’ is the probability of an object being classified to a particular class. It is the probability of misclassifying a randomly chosen element in a set. Just as the trees are a vital part of human life, tree-based algorithms are an important part of machine learning. After that, calculate the entropy of each attribute ( Color and Shape). A decision tree is a specific type of flow chart used to visualize the decision-making process by mapping out the different courses of action, as well as their potential outcomes. pruning: how to judge, what to prune (tree, rules, etc. Each subset should contain data with the same value for an attribute. Jan 5, 2022 · Jan 5, 2022. When a leaf is reached, we return the classi cation on that leaf. This process allows companies to create product roadmaps, choose between May 31, 2024 · Decision trees are a popular machine learning algorithm that can be used for both regression and classification tasks. New nodes added to an existing node are called child nodes. In contrast, decision trees perform relatively well even when the assumptions in the dataset are only partially fulfilled. Step 1: Import necessary libraries and generate synthetic data. Assume: I am 30 Problem Definition: Build a decision tree using ID3 algorithm for the given training data in the table (Buy Computer data), and predict the class of the following new example: age<=30, income=medium, student=yes, credit-rating=fair. predict(iris. avoiding: stopping early, pruning. Simple! To predict class labels, the decision tree starts from the root Dec 11, 2019 · Building a decision tree involves calling the above developed get_split () function over and over again on the groups created for each node. It is the measure of impurity, disorder, or uncertainty in a bunch of data. Start with the main decision. (Example is taken from Data Mining Concepts: Han and Kimber) #1) Learning Step: The training data is fed into the system to be analyzed by a classification algorithm. Machine Learning Modeling Decision Tree posted by ODSC Community December 7, 2021. We traverse down the tree, evaluating each test and following the corresponding edge. Here’s how a decision tree model works: 1. Let’s explain the decision tree structure with a simple example. In learning a decision tree, we must rst choose a root attribute and then recur-sively decide sub-roots, building the decision tree in a top-down fashion. Decision Tree Classifier – Python Code Example. data[removed]) # assign removed data as input. Events with higher uncertainty have higher entropy. Option 2: replace that part of the tree with a leaf corresponding to the most frequent label in the data S going to that part of the tree. Repeat step 1 & step 2 on each subset. UC Berkeley (link resides outside ibm. (a)[1 point] We can get multiple local optimum solutions if we solve a linear regression problem by minimizing the sum of squared errors using gradient descent. Apr 18, 2024 · Inference of a decision tree model is computed by routing an example from the root (at the top) to one of the leaf nodes (at the bottom) according to the conditions. Sep 6, 2011 · R. Figure 3 visualizes our decision tree learned at the first stage of ID3. A solution for it is clipping. branches. Feb 6, 2020 · Decision Tree Algorithm Pseudocode. Decision Trees & Machine Learning. Decision tree is also easy to interpret and understand compared to other ML algorithms. 2 represents the change in entropy as the proportion of the number of instances belonging to a particular class It continues the process until it reaches the leaf node of the tree. In information theory, a random variable’s entropy reflects the average uncertainty level in its possible outcomes. 2. com/watch?v=gn8 Decision trees are a versatile and powerful tool in the machine learning arsenal. We also navigated through common roadblocks, offering practical solutions to enhance model Feb 17, 2023 · Key Concepts – Decision Trees. Practice Test on Decision Trees Concept. income. Mathematics behind decision tree is very easy to understand compared to other machine learning algorithms. Therefore, in this book, the decision tree is defined as a supervised learning model that hierarchically maps a May 2, 2024 · In this section, we aim to employ pruning to reduce the size of decision tree to reduce overfitting in decision tree models. Information theory finds applications in machine learning models, including Decision Trees. Decision trees are intuitive. prediction = clf. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. Nov 25, 2020 · A Decision Tree has many analogies in real life and turns out, it has influenced a wide area of Machine Learning, covering both Classification and Regression. Standardization) Decision Regions. If you are just getting started with machine learning, it’s […] On this page, we collected 10 best open source license classification tree software solutions that run on Windows, Linux, and Mac OS X. The ultimate goal is to create a model that predicts a target variable by using a tree-like pattern of decisions. Decision trees overfit Solution: 3. Decision trees use various algorithms to split a dataset into homogeneous (or pure) sub-nodes. The goal of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data (training data). Feb 27, 2023 · Decision Trees are the foundation for many classical machine learning algorithms like Random Forests, Bagging, and Boosted Decision Trees. And why not, after all, we all are consumers of ML directly or indirectly In machine learning a decision tree is an algorithm used for either of the two tasks, Regression, and Classification. Entropy of the set S. Jul 12, 2024 · The final prediction is made by weighted voting. Advantages: Decision cedar are superb interpretable; Require few details preprocessing; Suitable for low maximum applications; Disadvantages: Further likely to overfit noisy data. In other words, the results of a decision tree are a flow chart with either a probability or a class returned based on the conditions of the observation. They offer interpretability, versatility, and simple visualization, making them valuable for both categorization and regression tasks. Algorithm for Random Forest Work: Step 1: Select random K data points from the training set. What are the effects of replacing the numerical features with their negative values (for example, changing the value +8 to -8) with the exact numerical splitter? The structure of the decision tree will be completely different. fit(new_data,new_target) # train data on new data and new target. Classically, this algorithm is referred to as “decision trees”, but on some platforms like R they are referred to by . Here , we generate synthetic data using scikit-learn’s make_classification () function. Aug 6, 2023 · The biggest issue of decision trees in machine learning is overfitting, which can lead to wrong decisions. They’re simple to understand and can be drawn and explained visually Data: data D, feature set Result: decision tree if all examples in D have the same label y, or is empty and y is the best guess then return Leaf(y); else for each feature in do partition D into D0 and D1 based on let mistakes(. Decision Tree 2. Photo by Simon Wilkes on Unsplash. ‣Allows us to design algorithms. fig 2. Developed in the early 1960s, decision trees are primarily used in data mining, machine learning and t. The range of entropy is [0, log (c)], where c is Decision Tree Analysis is a general, predictive modelling tool that has applications spanning a number of different areas. Instability: Decision trees can be unstable, meaning that small changes in the data can result in different trees. Pandas has a map() method that takes a dictionary with information on how to convert the values. This makes it complex to interpret, and it loses its generalization capabilities. Decision trees are vital in the field of Machine Learning as they are used in the process of predictive modeling. The first split to obtain the decision tree model in Figure 11. In this article, we'll learn about the key characteristics of Decision Trees. It’s a graphical representation of a decision-making process that involves splitting data into subsets based on certain conditions. 2 pc c2C. Assume our data is a set S of examples with C many classes is the probability that a random element of S belongs to class c. , facial recognition) 2. This means that Decision trees are flexible models that don’t increase their number of parameters as we add more features (if we build them correctly), and they can either output a categorical prediction (like if a plant is of c) At each node, the successor child is chosen on the basis of a splitting of the input space. It is a supervised learning algorithm used for both classification and regression tasks in machine learning. View Answer. They’re often used by organizations to help determine the most optimal course of action by comparing all of the possible consequences of making a set of decisions. a) True. Using the given data, one possible decision tree is shown in Figure 3b. 2 : Entropy graph for 2 classes. “loan decision”. (1) Question: Explain the principle of the gradient descent algorithm. To put it more visually, it’s a flowchart structure where different nodes indicate conditions, rules, outcomes and classes. CART (Classification And Regression Tree) is a decision tree algorithm variation, in the previous article — The Basics of Decision Trees. As you can see from the diagram below, a decision tree starts with a root node, which does not have any Mar 8, 2020 · Introduction and Intuition. It is a supervised learning algorithm that learns from labelled data to predict unseen data. Akerkar 4. Option 1: leaving the tree as is. In a decision tree, each leaf node represents a rule. A Decision Tree • A decision tree has 2 kinds of nodes 1. 5. , picking stocks) ‣even when we don’t know how (e. We have learned how decision trees split their nodes and how they determine the quality of their splits. Furthermore, we have shown this through a few lines of code. We use entropy to measure the impurity or randomness of a dataset. Visually too, it resembles and upside down tree with protruding branches and hence the name. We have also mentioned the basic steps to build a decision tree. In this day and age, there is a lot of buzz around machine learning (ML) and artificial intelligence (AI). read_csv ("data. e. 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. Dec 5, 2022 · Decision Trees represent one of the most popular machine learning algorithms. This article delves into the role of decision tree nodes, exploring their types, functions, and Feb 9, 2022 · Unlike other supervised learning algorithms, the decision tree algorithm can be used for solving regression and classification problems too. This decision is depicted with a box – the root node. Tree structure: CART builds a tree-like structure consisting of nodes and branches. It is one of the most widely used and practical methods for supervised learning. The induction of decision trees is one of the oldest and most popular techniques for learning discriminatory models, which has been developed independently in the statistical (Breiman, Friedman, Olshen, & Stone, 1984; Kass, 1980) and machine Decision Trees Machine Learning. Tree models where the target variable can take a discrete set of values are called Jan 23, 2019 · Advantages and disadvantages of decision trees. Entropy is a measure of randomness/uncertainty of a set. By understanding their strengths and applications, practitioners can effectively leverage decision trees to solve a wide range of machine learning problems. Weka is a powerful collection of machine learning algorithms for data mining purposes. d) The splitting is based on one of the features or on a predefined set of splitting rules. First, we need to Determine the root node of the tree. 2, and the right of the line. For instance, in the example below Dec 7, 2021 · An Introduction to Decision Tree and Ensemble Methods. Handle Non-Linearity: Decision trees can handle non-linear relationships between features, which many other algorithms struggle with. Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. Based on the answers, either more questions are asked, or the classification is made. There are different algorithms to generate them, such as ID3, C4. True False Solution: False (b)[1 point] When a decision tree is grown to full depth, it is more likely to fit the noise in the data. In Machine Learning, prediction methods are commonly referred to as Supervised Learning. The random forest is a machine learning classification algorithm that consists of numerous decision trees. Aug 20, 2020 · Introduction. Each question is like a branch in a tree, and depending on the answer, you follow import pandas. A decision tree is a flowchart-like tree structure where an internal node represents a feature (or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The structure of a tree has given the inspiration to develop the algorithms and feed it to the machines to learn things we want them to learn and solve problems in real life. A decision tree is built in _______ fashion. In a visual representation, the branches represent the data Nov 13, 2020 · In a decision tree, entropy is a kind of disorder or uncertainty. Weka. #train classifier. Example: Here is an example of using the emoji decision tree. Step 3:Choose the number N for decision trees that you want to build. Their intuitive structure makes them popular for various applications, from predicting customer behavior to diagnosing medical conditions. The value of the reached leaf is the decision tree's prediction. Decision tree’s are one of many supervised learning algorithms available to anyone looking to make predictions of future events based on some historical data and, although there is no one generic tool optimal for all problems, decision tree’s are hugely popular and turn out to be very effective in many machine learning May 15, 2024 · Before it became a major part of programming, this approach dealt with the human concept of learning. Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. Using the Iris dataset, we saw these models in action, from simple visualizations to tackling complex data challenges. Machine Learning. X pc log. ) CS 5751 Machine Learning. Feb 10, 2021 · Introduction to Decision Trees. Moreover, when building each tree, the algorithm uses a random sampling of data points to train Dec 13, 2020 · If you are new to Machine Learning (ML) or are in the process of becoming a Data Scientist or ML practitioner like me, you will probably get something out of it. RULE 1 If it is sunny and the humidity is not above 75% then play 75%, play. ‣that predict the future (e. The training examples are sorted to the Aug 21, 2023 · A decision tree is a supervised machine learning algorithm used in tasks with classification and regression properties. 2 Split #2. Like most things, the machine learning approach also has a few disadvantages: Overfitting. Each decision tree in the random forest contains a random sampling of features from the data set. Probability vector p = [p1; p2; : : : ; pC] is the class distribution of the set S. To exploit the desirable properties of decision tree classifiers and perceptrons, Adam came up with a new algorithm called the “perceptron tree” that combines features from both. So we find leaf nodes in all the branches of the tree. leaf nodes, and. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. They offer interpretability, flexibility, and the ability to handle various data types and complexities. D1); D0) + (non-majority answers in. 2. These tree-based learning algorithms are considered to be one of the best and most used supervised Nov 29, 2023 · We went through the world of tree-based models in machine learning, uncovering the nuances of decision trees, random forests, and GBMs. Unlike the meme above, Tree-based algorithms are pretty nifty when it comes to real-world scenarios. com) breaks out the learning system of a machine learning algorithm into three main parts. If Examples vi , is empty. youtube. Apr 5, 2020 · 1. Each node represents an attribute (or feature), each branch represents a rule (or decision), and each leaf represents an outcome. Decision Tree Example We have five leaf nodes. The tree can be explained by two entities In practice, the decision tree-based supervised learning is defined as a rule-based, binary-tree building technique (see [ 1 – 3 ]), but it is easier to understand if it is interpreted as a hierarchical domain division technique. And probability of overfitting on noise increases as a tree gets deepens. These conditions are learned from the input features and their relationships with the target variable. The goal of the feature selection while building a decision tree is to find Sep 24, 2020 · 1. Decision Tree – ID3 Algorithm Solved Numerical Example by Mahesh HuddarDecision Tree ID3 Algorithm Solved Example - 1: https://www. Challenge in learning decision tree Exponentially many decision trees can be constructed from a given set of attributes – Some of the trees are more ‘accurate’ or better classifiers than the others – Finding the optimal tree is computationally infeasible Efficient algorithms available to learn a reasonably accurate (although potentially Jun 3, 2020 · Classification-tree. Decision Trees is the non-parametric Example 1: The Structure of Decision Tree. Training Phase: Mar 15, 2024 · Decision trees, a key tool in machine learning, model and predict outcomes based on input data through a tree-like structure. This first split resulted in a tree of one-level depth. Jul 3, 2021 · Decision trees are one of the simplest machine learning algorithms to not only understand but also implement. Let Examples vi, be the subset of Examples that have value vi for A. It is the most intuitive way to zero in on a classification or label for an object. In either case, here are the steps to follow: 1. If Trivially, there is a consistent decision tree for any training set w/ one path to leaf for each example (unless f nondeterministic in x) but it probably won’t generalize to new examples Need some kind of regularization to ensure more compact decision trees CS194-10 Fall 2011 Lecture 8 7 (Figure&from&StuartRussell)& Apr 7, 2016 · Decision Trees. Let us take an example with 2 Classes. Decision Tree is a supervised (labeled data) machine learning algorithm that Aug 16, 2023 · Advantages of Decision Trees. Credit rating. Overfitting: Decision trees are prone to overfitting, especially when the tree is deep and complex. search based on information gain (defined using entropy) favors short hypotheses, high gain attributes near root. e. In the Machine Learning world, Decision Trees are a kind of non parametric models, that can be used for both classification and regression. Explain the use of all the terms and constants that you introduce and comment on the range of values that they can take. y = df ['account_type'] Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. This can result in poor generalization performance on unseen data. Regression is a type of algorithm where we deal with continuous data such as Housing Prices, and Classification deals with discrete values where output is categorical. Sample Interview Questions on Decision Tree. Jan 8, 2024 · To build a decision tree, we need to calculate two types of Entropy- One is for Target Variable, the second is for attributes along with the target variable. In this example, the class label is the attribute i. Decision trees are commonly used in operations research, specifically in decision Dec 7, 2020 · The final step is to use a decision tree classifier from scikit-learn for classification. Nov 9, 2022 · A decision tree is a flowchart-like diagram mapping out all of the potential solutions to a given problem. Supervised Apr 18, 2024 · A decision tree model is a predictive modeling technique that uses a tree-like structure to represent decisions and their potential consequences. We have the following rules corresponding to the tree given in Figure. It performs well on the training data, but starts making mistakes on unseen data. This is a Java-based free and open source tool for Windows, Linux, and Mac OS X. In this example, a DT of 2 levels. Jul 18, 2022 · Question 1. Fitting a Decision Tree. Here, we'll briefly explore their logic, internal structure, and even how to create one with a few lines of code. The decision attribute for Root ← A. The first step is, we calculate the Entropy of the Target Variable (Fruit Type). Jun 12, 2021 · A decision tree is a machine learning model that builds upon iteratively asking questions to partition data and reach a solution. end. They are easy to understand, interpret, and implement, making them an ideal choice for beginners in the field of machine learning. A node may have zero children (a terminal node), one child (one side makes a prediction directly) or two child nodes. A decision tree will keep generating new nodes to fit the data. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes. Step-2: Find the best attribute in the dataset using Attribute Selection Measure (ASM). Step 2:Build the decision trees associated with the selected data points (Subsets). A decision tree can be used to build models for _______. Introduction Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The nodes represent different decision Click here to buy the book for 70% off now. (a) Example Data (b) Decision Tree Given these features, let’s further assume example data, given in Figure 3a. May 6, 2023 · In machine learning, a decision tree is a way to make predictions by asking a series of questions about the data. csv") print(df) Run example ». 2 Classifying an example using a decision tree Classifying an example using a decision tree is very intuitive. A decision tree is a tree-structured classification model, which is easy to understand, even by nonexpert users, and can be efficiently induced from data. 3. Sep 10, 2020 · Linear models perform poorly when their linear assumptions are violated. May 2, 2019 · Detecting Financial Fraud at Scale with Decision Trees and MLflow on Databricks. Classification and Regression Trees or CART for short is a term introduced by Leo Breiman to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems. 10-601 Machine Learning Exam 1 Practice Problems - Page 3 of 31 1 Decision Trees 1. It learns to partition on the basis of the attribute value. Jul 25, 2018 · Jul 25, 2018. Jul 15, 2024 · Classification and Regression Trees (CART) is a decision tree algorithm that is used for both classification and regression tasks. No matter what type is the decision tree, it starts with a specific decision. Detecting fraudulent patterns at scale using artificial intelligence is a challenge, no matter the use case. A tree can be seen as a piecewise constant approximation. RULE 2 If it is sunny and the humidity is above 75%, then do not play. It branches out according to the answers. df = pandas. Start with any variable, in this case, City Size. ‣Algorithms that use data to design algorithms. H(S) =. For the purposes of this page, we'll show one example of fitting a decision tree to predict whether an Instagram account is real or fake. For example, a decision tree could be used to help a company decide which Jan 2, 2020 · Figure 3: Partially learned Decision Tree from the first stage of ID3. 3 partitioned the complete data set into two groups, represented by the points to the left of the vertical line in Figure 11. All they do is ask questions, like is the gender male or is the value of a particular variable higher than some threshold. Sequence of if-else questions about individual features. Decision tree uses the inductive learning machine learning approach. The topmost node in a decision tree is known as the root node. While entropy measures the amount of uncertainty or randomness in a set. Jul 23, 2023 · In a nutshell, decision trees are a type of machine learning algorithm that make decisions by asking a series of questions. The massive amounts of historical data to sift through, the complexity of the constantly evolving machine learning and deep learning techniques, and the May 10, 2024 · Decision tree is used in data mining, machine learning, and statistics. May 27, 2024 · Decision trees are a fundamental part of machine learning, used for both classification and regression tasks. Each decision tree has 3 key parts: a root node. Oct 1, 2023 · A decision tree is a supervised machine learning algorithm that resembles a flowchart-like structure. --. Accompany your explanation with a diagram. True False Solution: True 11. Perceptron trees are similar to decision trees, but each leaf node contains a Jan 3, 2023 · A decision tree is a supervised machine learning algorithm that creates a series of sequential decisions to reach a specific result. The best attribute of the dataset should be placed at the root of the tree. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. 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. label = most common value of Target_attribute in Examples. They are non-parametric supervised learning methods that can be used for both regression and classification tasks. The depth of a Tree is defined by the number of levels, not including the root node. Apr 17, 2019 · DTs are composed of nodes, branches and leafs. Sep 25, 2023 · A Decision tree is a data structure consisting of a hierarchy of nodes that can be used for supervised learning and unsupervised learning problems ( classification, regression, clustering, …). Nowadays, decision tree analysis is considered a supervised learning technique we use for regression and classification. To make a decision tree, all data has to be numerical. It is a way to control the split of data decided by a decision tree. issues: overfitting. You can draw it by hand on paper or a whiteboard, or you can use special decision tree software. -values; ) = (non-majority answers in. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. g. The decision tree provides good results for classification tasks or regression analyses. EXAMPLE Machine Learning (C395) Exam Questions. The same conditions will be learned; only the positive/negative children will be switched. Demo. Jul 4, 2021 · fig 2. The Decision Tree is a machine learning algorithm that takes its name from its tree-like structure and is used to represent multiple decision stages and the possible response paths. Decision region: region in the feature space where all instances are assigned to one class label May 31, 2024 · Entropy measures the amount of surprise and data present in a variable. May 10, 2024 · Example of Creating a Decision Tree. 4 Disadvantages of decision trees. Decision trees combine multiple data points and weigh degrees of uncertainty to determine the best approach to making complex decisions. The algorithmic approach constructs the decision tree based on distinct conditions and finds a way of splitting the data. In my case, preparing the material for the article helped me gain a good understanding of how Random Forests and Decision Trees work under the hood without getting into complex code. clf = tree. DecisionTreeClassifier() # defining decision tree classifier. For example, consider the following feature values: num_legs. 5 and CART. Interpretability: Decision trees are easy to understand and interpret, making them a valuable tool for both beginners and experts in the field of machine learning. Chapter 3 Decision Tree Learning. cj so wp ps gy mu uc gj bd nm
