How to read a decision tree in python. html>re

A decision tree begins with the target variable. Follow the code to import the required packages in python. Jul 30, 2017 · I'm doing some feature induction with decision trees and would like to know the size of the tree in terms of number of nodes. Building a Decision Tree in Python demystifies the process of data analysis and machine learning, making it accessible even to beginners. Oct 30, 2019 · The goal is to predict which room the phone is located in based on the strength of Wi-Fi signals 1 to 7. datasets import load_iris. Split the training set into subsets. Nov 19, 2023 · Chapter 8: Implementing a Decision Tree in Python. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for your model, how to… Read More »Decision Tree Classifier with Nov 13, 2017 · 7. 📚 Programming Books & Merch 📚🐍 The Python Bible Book: https://www. fit(X, y) # plot tree. gumroad. label = most common value of Target_attribute in Examples. One starts at the root node, where the first question is asked. In this tutorial you will discover how you can plot individual decision trees from a trained gradient boosting model using XGBoost in Python. Decision Tree model Advantages and Disadvantages. Apr 21, 2017 · graphviz web portal. A trained decision tree of depth 2 could look like this: Trained decision tree. Calculate Gini impurity for sub-nodes, using the formula subtracting the sum of the square of probability for success and failure from one. Step 2 – Types of Tree Visualizations. Visualize the Decision Tree with graphviz. The result of clf. Display the top five rows from the data set using the head () function. export_dict() function seems to be exactly what I'm looking for, but I can't figure out how to call it (keep getting an AttributeError: 'module' object has no attribute 'export_dict'). The iris data set contains four features, three classes of flowers, and 150 samples. Predictions are performed by traversing the tree from root to leaf and going left when the condition is true. 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. This tutorial will explain boosted trees in a self-contained and principled way using the elements of supervised learning. Oct 26, 2020 · Step-1: Importing the packages. To create a decision tree in Python, we use the module and the corresponding example from the documentation. We think this explanation is cleaner, more formal, and motivates the model formulation used in XGBoost. tree import _tree. Repeat step 1 and step 2 on each subset until you find leaf nodes in all the branches of the tree. node_indicator = estimator. # method allows to retrieve the node indicator functions. Remove the already presented text in the text box and paste the text in the created txt file and click on the generate-graph button. This is usually called the parent node. How classification trees make predictions. May 17, 2024 · A decision tree is a flowchart-like structure used to make decisions or predictions. Features: sepal length (cm), sepal width (cm), petal length (cm), petal width (cm) Numerically, setosa flowers are identified by zero, versicolor by one, and Place the best attribute of our dataset at the root of the tree. – Preparing the data. metrics import accuracy_score import matplotlib. This a Churn model result. How to Interpret Decision Trees with 1 Simple Example. Figure 17. Jul 30, 2022 · Here we are simply loading Iris data from sklearn. Subsets should be made in such a way that each subset contains data with the same value for an attribute. You need to use the predict method. The Skicit-Learn Python module provides a variety of tools needed for data analysis, including the decision tree. Jan 1, 2023 · Final Decision Tree. Note that the new node on the left-hand side represents samples meeting the deicion rule from the parent node. Then you can open a picture and zoom to the specific nodes to inspect them. The CHAID algorithm uses the chi-square metric to determine the most important features and recursively splits the dataset until sub-groups have a single decision. It aims to enhance model performance by reducing overfitting, improving interpretability, and cutting computational complexity. Feb 19, 2020 · This decision tree tutorial discusses how to build a decision tree model in Python. In addition, decision tree models are more interpretable as they simulate the human decision-making process. Each internal node corresponds to a test on an attribute, each branch Apr 1, 2020 · In order to visualize decision trees, we need first need to fit a decision tree model using scikit-learn. Let Examples vi, be the subset of Examples that have value vi for A. 5, and CHAID, are available for constructing decision trees, each employing different criteria for node splitting. tree_. Let’s start from the root: The first line “petal width (cm) <= 0. Key Terminology. Our primary packages involved in building our model are pandas, scikit-learn, and NumPy. Jul 17, 2021 · The main disadvantage of random forests is their lack of interpretability. Feb 12, 2022 · 0. After training the tree, you feed the X values to predict their output. Decisions Trees is a powerful group of supervised Machine Learning models that can be used for both classification and regression. Jan 29, 2020 · Among decision support tools, decision trees (and influence diagrams) have several advantages. Feb 22, 2019 · A Scikit-Learn Decision Tree. Aug 31, 2017 · type(graph) <type 'list'>. Supervised learning. The maximum depth can be specified in the XGBClassifier and XGBRegressor wrapper classes for XGBoost in the max_depth parameter. You’ll only have to implement two formulas for the learning part — entropy and information gain. Among other things, it is based on the data formats known from Numpy. figure(figsize=(12,12)) # set plot size (denoted in inches) tree. May 14, 2024 · Decision Tree is one of the most powerful and popular algorithms. This concept, originating from information theory, is crucial for effective decision-making in various machine learning applications. Assume that our data is stored in a data frame ‘df’, we then can train it Apr 15, 2020 · Scikit-learn 4-Step Modeling Pattern. May 29, 2022 · Today we learn how to visualize decision trees in Python. Steps to Calculate Gini impurity for a split. # This was already imported earlier in the notebook so commenting out. Another disadvantage is that they are complex and computationally expensive. To make the rules look more readable, use the feature_names argument and pass a list of your feature names. The gradient boosted trees has been around for a while, and there are a lot of materials on the topic. I prefer Jupyter Lab due to its interactive features. 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. The tree look like as picture below. Decision Trees # Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for your model, how to… Read More »Decision Tree Classifier with Feb 19, 2023 · The process of building a decision tree involves selecting an attribute at each node that best splits the data into homogeneous groups. Step 3: Training the decision tree model. Feb 16, 2022 · Let’s code a Decision Tree (Classification Tree) in Python! Coding a classification tree I. Subscribe: https://www. 1. You can see below, train_data_m is our dataframe. So you can do this one of following of two ways, 1) Change line where you collect dot_data value in graph to. target) tree. If splitting criteria are satisfied, then each node has two linked nodes to it: the left node and the right node. We’ll use the zoo dataset from Tomi Mester’s previous pandas tutorial articles. In decision tree classifier, the Mar 27, 2021 · Step 3: Reading the dataset. The random forest is a machine learning classification algorithm that consists of numerous decision trees. csv') X=music_d. Using Python. plot_tree() to display the resulting decision tree: model. graph_from_dot_data(dot_data. Now let us see the python implementation of both Decision tree and Random forest models with the help of a telecom churn data set. target, iris. clf = DecisionTreeClassifier (max_depth=3) #max_depth is maximum number of levels in the tree. Step 4: Evaluating the decision tree classification accuracy. 1- (p²+q²) where p =P (Success) & q=P (Failure) Calculate Gini for Jul 27, 2019 · y = pd. Load the data set using the read_csv () function in pandas. 1: Addressing Categorical Data Features with One Hot Encoding. Each decision tree in the random forest contains a random sampling of features from the data set. The code below is based on StackOverflow answer - updated to Python 3. A python library for decision tree visualization and model interpretation. It helps determine node splitting in the tree, aiming for maximum information gain and minimal entropy. Note: Both the classification and regression tasks were executed in a Jupyter iPython Notebook. All the code can be found in a public repository that I have attached below: NOTE: You can support StatQuest by purchasing the Jupyter Notebook and Python code seen in this video here: https://statquest. 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. Colab shows that the root condition contains 243 examples. We need to write it. Plot Tree with plot_tree. The topmost node in a decision tree is known as the root node. The from-scratch implementation will take you some time to fully understand, but the intuition behind the algorithm is quite simple. Based upon the answer, we navigate to one of two child nodes. Jul 19, 2021 · Timestamps0:00 - 0:23 Intro0:23 - 0:55 What Does A Decision Tree Look Like?0:56 - 1:50 A Deep Dive Into Our Dataset1:51 - 2:26 How do Decision Trees Come Up Aug 19, 2018 · There are 4 methods which I'm aware of for plotting the scikit-learn decision tree: The simplest is to export to the text representation. Writing algorithms with libraries such as scikit-learn, matplotlib, and graphviz are used for Nov 3, 2023 · In decision tree regression, the algorithm builds a tree-like structure to predict a continuous target variable. # through the node j. This section guides you through creating your first Decision Tree using Python, emphasizing practical experience and clarity. Step #3: Based on the impurity measures, choose the single best split. y_pred = clf. From the analysis perspective the first node is the root node, which is the first variable that splits the target variable. Decision tree algorithm is used to solve classification problem in machine learning domain. That caused the split of data we see in the second row and we can easily see and understand the remaining splits until the algorithm finishes at a depth of three with 3 groups classified as white wine Nov 22, 2021 · Classification and Regression Trees (CART) can be translated into a graph or set of rules for predictive classification. getvalue()) 2) Or collect entire list in graph but just use first element to be sent to pdf. Update Mar/2018: Added alternate link to download the dataset as the original appears […] Jan 12, 2022 · A Decision Tree algorithm is a supervised learning algorithm for classification and regression tasks. Once you've fit your model, you just need two lines of code. #from sklearn. Decision trees, non-parametric supervised learning algorithms, are explored from basics to in-depth coding practices. They help when logistic regression models cannot provide sufficient decision boundaries to predict the label. data) Apr 17, 2022 · In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. The treatment of categorical data becomes crucial during the tree 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. It can be used to predict the outcome of a given situation based on certain input parameters. fit (breast_cancer. Key concepts such as root nodes, decision nodes, leaf nodes, branches, pruning, and parent-child node Mar 11, 2024 · Feature selection involves choosing a subset of important features for building a model. ensemble import RandomForestClassifier clf = RandomForestClassifer(n_estimators = 10) clf = clf. Let’s see what a decision tree looks like, and how they work when a new input is given for prediction. Let’s start with entropy. How do I do that in python? Using the stock example from sklearn's website, x = [[0,0],[0,1]] y = [0,1] from sklearn. Visualizing decision trees is a tremendous aid when learning how these models work and when Sep 10, 2015 · 17. data, iris. (graph, ) = pydot. tree import export_text. Categorical. 10. If this section is not clear, I encourage you to check out my Understanding Decision Trees for Classification (Python) tutorial ( blog , video ) as I go into a lot of detail on how decision trees work and how to use them. Please help me plot a tree of higher resolution as the image gets blurred when I increase the tree depth. Dec 22, 2019 · clf. plot_tree() In Colab, you can use the mouse to display details about specific elements such as the class distribution in each node. # Step 1: Import the model you want to use. For the modeled fruit classifier, we will get the below decision tree visualization. Step #4: Partition using the best splits recursively until the stopping condition is met. Datasets can have hundreds, thousands, or sometimes millions of features in the case of image- or text-based models. The decision attribute for Root ← A. 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. e. 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. read_csv('music. 3. X = data. In this article, We are going to implement a Decision tree in Python algorithm on the Balance Scale Weight & Distance This video covers the basics of decision trees and how to make decision trees for classification in Python. decision tree visualization with graphviz. First, import export_text: from sklearn. You can do something like the following: Theory. It’s only a few rows (22) but will be perfect to learn how to build a classification tree with scikit-learn. We are only interested in first element of the list. The next video will show you how to code a decisi Decision trees are a non-parametric model used for both regression and classification tasks. Separate the independent and dependent variables using the slicing method. People are able to understand decision tree Mar 28, 2024 · Building Your First Decision Trees in Python. show() If you want to capture structure of the whole tree I guess saving the plot with small font and high dpi is the solution. It works for both continuous as well as categorical output variables. Machine learning still suffers from a black box problem, and one image is not going to solve the issue!Nonetheless, looking at an individual decision tree shows us this model (and a random forest) is not an unexplainable method, but a sequence of logical questions and answers — much as we would form when making predictions. Jul 1, 2018 · The decision_path. 4. datasets and training a very simple Decision Tree for visualizing it further. qualities of a house) will be used to predict a continuous output (e. Asking for help, clarification, or responding to other answers. Decision trees are constructed by recursively partitioning the data based on the values of features until a stopping criterion is met. data, breast_cancer. predict(iris. Mar 27, 2024 · Python is a great tool for building a decision tree. target_names) In the proceeding section, we’ll attempt to build a decision tree classifier to determine the kind of flower given its dimensions. With the head() method of the Apr 14, 2021 · The first node in a decision tree is called the root. In Python, we can use the scikit-learn method DecisionTreeClassifier for building a Decision Tree for classification. head() Although, decision trees can handle categorical data, we still encode the targets in terms of digits (i. Aug 27, 2020 · Plotting individual decision trees can provide insight into the gradient boosting process for a given dataset. For example, a very simple decision tree with one root and two leaves may look like this: 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. If you want to implement a decision tree from scratch I recommend you to build your tree using classes. pyplot as plt # create tree object model_gini_class = tree. predict_proba(X) is: The predicted class probability which is the fraction of samples of the same class in a leaf. Throughout this article, I’ll walk you through training a Decision Tree in Python using scikit-learn on the Iris Species Dataset, known as In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package. If Examples vi , is empty. You'll also learn the math behind splitting the nodes. decision_path(X_test) # Similarly, we can also have the leaves ids reached by each sample. # indicator matrix at the position (i, j) indicates that the sample i goes. 2: Splitting the dataset. How to use scikit-learn (Python) to make classification trees. For example, if Wifi 1 strength is -60 and Wifi 5 Apr 17, 2022 · In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. Throughout this article, I’ll walk you through training a Decision Tree in Python using scikit-learn on the Iris Species Dataset, known as 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. We can split up data based on the attribute Jan 22, 2022 · Jan 22, 2022. May 2, 2024 · Let's implement decision trees using Python's scikit-learn library, focusing on the multi-class classification of the wine dataset, a classic dataset in machine learning. # Create Decision Tree classifier object. Provide details and share your research! But avoid …. A node may have zero children (a terminal node), one child (one side makes a prediction directly) or two child nodes. Splitting: The algorithm starts with the entire dataset Jan 13, 2021 · Here, I've explained Decision Trees in great detail. DecisionTreeClassifier(criterion='gini Mar 4, 2024 · The role of categorical data in decision tree performance is significant and has implications for how the tree structures are formed and how well the model generalizes to new data. tree import DecisionTreeClassifier from sklearn import tree classifier = DecisionTreeClassifier(max_depth = 3,random_state = 0) tree. Aug 23, 2023 · A decision tree is a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents an outcome or a class label. 5 (Integer) 2. Apr 8, 2021 · Decision trees are a non-parametric model used for both regression and classification tasks. Dec 28, 2023 · Also read: Decision Trees in Python. Let’s get started. plot_tree: Dec 4, 2022 · How to plot decision tree graph in python sklearn (visualization and interpretation) - decision tree visualization interpretation NumPy Tut Aug 7, 2018 · I built a Decision Tree in python and I am struggling to interpret it. Step 5: (sort of optional) Optimizing the hyperparameters. com Jan 21, 2019 · The sklearn. Oct 8, 2021 · Performing The decision tree analysis using scikit learn. gini: we will talk about this in another tutorial. Feb 5, 2020 · Decision Tree. The first node from the top of a decision tree diagram is the root node. We will also pass the features and classes names, and customize the plot so that each tree node is displayed Jan 5, 2022 · Train a Decision Tree in Python. The options are “gini” and “entropy”. The related part of the code is presented below: # TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature new_data = data. Let’s change a couple of parameters to see if there is any effect on the accuracy and also to make the tree shorter. DecisionTreeClassifier(criterion = "entropy") dtree = dtree. Once a node has been split, the process is Sep 11, 2014 · 6. Tree depth isn't an issue in my case, since I've set max_depth = 2 – Nov 7, 2023 · First, we’ll import the libraries required to build a decision tree in Python. It serves as the foundation for more sophisticated models like Random Forest, Gradient Boosting, and XGBoost. from sklearn. Once the graphviz web portal opened. Let’s break down the process: 1. For the case of a binary tree, these classes can be something like: class Node(object): def __init__(self): Apr 8, 2021 · Math Behind Decision Trees. Let me try to write about it with 750 characters. We can visualize the Decision Tree in the following 4 ways: Printing Text Representation of the tree. fit(x,y) Jan 22, 2023 · Step 2: Prepare the dataset. import graphviz. . It learns to partition on the basis of the attribute value. Implementing a decision tree in Python involves understanding several key concepts and translating them into code. Decision trees are an intuitive supervised machine learning algorithm that allows you to classify data with high degrees of accuracy. com/c/DataDaft?sub Dec 30, 2023 · The Decision Tree stands as one of the most famous and fundamental Machine Learning Algorithms. Criterion: defines what function will be used to measure the quality of a split. Entropy in decision trees is a measure of data purity and disorder. model_selection import cross_val_score from sklearn. Sep 10, 2017 · I am trying to evaluate a relevance of features and I am using DecisionTreeRegressor(). Step #5: Prune the decision tree. Dec 24, 2023 · The Decision Tree stands as one of the most famous and fundamental Machine Learning Algorithms. neuralnine. In the following examples we'll solve both classification as well as regression problems using the decision tree. Step 2. Then below this new branch add a leaf node with. Decision Tree for Classification. Decision trees, being a non-linear model, can handle both numerical and categorical features. clf = DecisionTreeClassifier(max_depth = 2, random_state = 0)# Step 3: Train the model on the data. Jun 20, 2022 · How to Interpret the Decision Tree. We are going to read the dataset (csv file) and load it into pandas dataframe. Jun 22, 2022 · CART (Classification and Regression Tree) uses the Gini method to create binary splits. target) Jan 11, 2023 · Python | Decision Tree Regression using sklearn. Setting Up Your Python Environment. tree import DecisionTreeClassifier# Step 2: Make an instance of the Model. tree import DecisionTreeClassifier. clf = clf. the price of that house). You pass the fit model into the plot_tree() method as the main argument. Apr 18, 2024 · Call model. fit(iris. Number of children at home <=3. fit (X_train,y_train) #Predict the response for test dataset. Let’s start by creating decision tree using the iris flower data se t. For each possible value, vi, of A, Add a new tree branch below Root, corresponding to the test A = vi. Moreover, when building each tree, the algorithm uses a random sampling of data points to train May 15, 2020 · Am using the following code to extract rules. The nodes at the bottom of the tree are called leaves. I was expecting either MaritalStatus_M=0 or =1) 3. plot_tree(classifier); 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. If it Click here to buy the book for 70% off now. A non zero element of. There isn't any built-in method for extracting the if-else code rules from the Scikit-Learn tree. One cannot trace how the algorithm works unlike decision trees. Decision-tree algorithm falls under the category of supervised learning algorithms. setosa=0, versicolor=1, virginica=2 Nov 2, 2022 · Flow of a Decision Tree. The algorithm creates a model of decisions based on given data, which can then be applied to unseen data to make predictions. A tree is composed of nodes, where one node contains nodes recursively and leafs are terminal nodes. 8” is the decision rule applied to the node. Just Re-install Anaconda with the latest version and use this code: import pandas as pd. This tree seems pretty long. plot_tree(clf, fontsize=10) plt. music_d=pd. After importing Aug 18, 2018 · Conclusions. With the rise of the XGBoost library, Decision Trees have been some of the Machine Learning models to deliver the best results at competitions. Second, create an object that will contain your rules. from_codes(iris. 5 (M- Married in here and was a binary. MaritalStatus_M <= 0. def tree_to_code(tree, feature_names): tree_ = tree. You will learn how to build a decision tree, how to prune a decision tree Dec 13, 2020 · In that article, I mentioned that there are many algorithms that can be used to build a Decision Tree. I want to know how can I interpret the following: 1. Interpretation of the results: The first print returns ['male' 'male'] so the data [[68,9],[66,9]] are predicted as males. drop(columns=['genre']) y=music_d['genre'] model=DecisionTreeClassifier() Continuous Variable Decision Trees: In this case the features input to the decision tree (e. ix[:,"X0":"X33"] dtree = tree. clf. Hyperparameter tuning. Decision trees are constructed from only two elements – nodes and branches. In this tutorial we will solve employee salary prediction problem Nov 16, 2023 · In this section, we will implement the decision tree algorithm using Python's Scikit-Learn library. Bonus Step 6: Visualizing the decision tree. A decision tree trained with default hyperparameters. 25) using the given feature as the target # TODO: Set a random state. youtube. drop(['Frozen'], axis = 1) # TODO: Split the data into training and testing sets(0. X. Max_depth: defines the maximum depth of the tree. clf = DecisionTreeClassifier () # Train Decision Tree Classifier. As mentioned earlier, it measures a purity of a split at a node level. Note, that scikit-learn also provides DecisionTreeRegressor, a method for using Decision Trees for Regression. Feb 25, 2021 · Extract Code Rules. Decision trees represent much more of a coding challenge than a mathematical one. tree. fit(X, Y) 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. Jan 30, 2021 · Reading from the top the decision tree machine learning algorithm chose make the first data split based on total sulfur dioxide less than 74. My tree plot looks squished: Below are my code: from sklearn import tree from sklearn. Jul 31, 2019 · Additionally, this tutorial will cover: The anatomy of classification trees (depth of a tree, root nodes, decision nodes, leaf nodes/terminal nodes). New nodes added to an existing node are called child nodes. com/l/tzxohThis webinar Jan 1, 2020 · Simple decision tree with a max depth of 2 and accuracy of 79. 1%. clf = DecisionTreeClassifier(random_state=0) iris = load_iris() tree = clf. The most commonly used metric for selecting the best attribute is information gain, which measures the reduction in entropy or disorder in the data after the split. Decision trees are constructed from only two elements — nodes and branches. Jul 12, 2020 · Step #2: Go through each feature and the possible splits. Here’s how it works: 1. The Decision Tree then makes a sequence of splits based in hierarchical order of impact on this target variable. plt. 2. Mar 2, 2019 · This article is made for complete beginners in Machine Learning who want to understand one of the simplest algorithm, yet one of the most important because of its interpretability, power of prediction and use in different variants like Random Forest or Gradient Boosting Trees. predict (X_test) 5. Python Decision-tree algorithm falls under the category of supervised learning algorithms. g. Dec 4, 2019 · I am trying to plot a plot_tree object from sklearn with matplotlib, but my tree plot doesn't look good. 50. Each child node asks an additional question, and based upon May 3, 2021 · Various algorithms, including CART, ID3, C4. We can interpret Decision Trees as a sequence of simple questions for our data, with yes/no answers. Decision trees: Are simple to understand and interpret. Aug 27, 2020 · Generally, boosting algorithms are configured with weak learners, decision trees with few layers, sometimes as simple as just a root node, also called a decision stump rather than a decision tree. The example decision tree will look like: Then if you have matplotlib installed, you can plot with sklearn. Apr 18, 2023 · Now, to plot the tree and get the underlying splits made by the model, we'll use Scikit-Learn's plot_tree() method and matplotlib to define a size for the plot. Decision Tree Classifier is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. Nov 26, 2018 · Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. iu ll el jg ap yt sk td re ew