Decision tree classifier sklearn example. Stack of estimators with a final classifier.

9. When using either a smaller dataset or a restricted depth, this may speed up the training. Sep 9, 2020 · Visualization of Decision Tree: Let’s import the following modules for Decision Tree visualization. The decision-tree algorithm is classified as a supervised learning algorithm. 8 in some column are classified as class 0. More about leaves and nodes later. The tree_. Here we use the well-known Iris species dataset to illustrate how SHAP can explain the output of many different model types, from k-nearest neighbors, to neural networks. A slight change in the data can drastically change the tree and, consequently the final results[1]. Also known as one-vs-all, this strategy consists in fitting one classifier per class. 2. DecisionTreeClassifier. Second, create an object that will contain your rules. Plot decision boundary given an estimator. Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data. The problem with coding categorical variables as integers, as you have To use this feature, feed the classifier an indicator matrix, in which cell [i, j] indicates the presence of label j in sample i. Refer to the example entitled Nearest Neighbors Classification showing the impact of the weights parameter on the decision boundary. Nov 16, 2023 · An example of classification is sorting a bunch of different plants into different categories like ferns or angiosperms. The reason for this is that it leverages multiple instances of another algorithm at the same time to find a result. 8, where continuous values under 0. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a In this chapter, we will learn about learning method in Sklearn which is termed as decision trees. GridSearchCV implements a “fit” and a “score” method. frame. I start out with a pandas. At prediction time, the class which received the most votes is selected. criterion{“gini”, “entropy”}, default=”gini”. Choosing min_resources and the number of candidates#. # through the node j. A decision tree classifier. Decisions tress (DTs) are the most powerful non-parametric supervised learning method. A decision tree is a tree-like structure that represents a series of decisions and their possible consequences. We often use this type of decision-making in the real world. Cost complexity pruning provides another option to control the size of a tree. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical 3. The decision trees can be divided, with respect to the target values, into: Classification trees used to classify samples, assign to a limited set of values - classes. externals. For this data set, when you binarize your label, you need to apply the classification three times. Then we will implement an end-to-end project with a dataset to show an example of Sklean decision tree classifier with DecisionTreeClassifier() function. AdaBoostClassifier All you need to do is select a number of estimators, and it will very quickly—in parallel, if desired—fit the ensemble of trees (see the following figure): [ ] from sklearn. The example below trains a decision tree classifier using three feature vectors of length 3, and then predicts the result for a so far unknown fourth feature vector, the so called test vector. At each internal node of the tree, a decision is made based on a specific feature, leading to one of its child nodes. This article delves into the components, terminologies, construction, and advantages of decision trees, exploring their Boosting algorithms combine multiple low accuracy (or weak) models to create a high accuracy (or strong) models. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Dec 21, 2015 · Some quick preliminaries: Let's say we have a classification problem with K classes. 1. P. There is no way to handle categorical data in scikit-learn. Dec 19, 2017 · Well, I am surprised, but it turns out that sklearn's decision tree cannot handle categorical data indeed. You'll do so using all the 30 features in the dataset, which is split into 80% train and 20% test. export_graphviz(clf, out_file=None, feature_names=None, class_names=target_col, filled=True) # Draw graph graph = graphviz. OneVsRestClassifier(estimator, *, n_jobs=None, verbose=0) [source] #. One-vs-the-rest (OvR) multiclass strategy. In information retrieval, precision is a measure of result relevancy, while recall is a measure In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package. Semi-supervised learning#. The first step is to load the dataset: This is a simple multi-class classification dataset for wine recognition. Decision trees can be used for either classification or regression tasks. This is a popular supervised model used for both classification and regression and is a useful way to understand distance functions, voting systems, and hyperparameter optimization. Step 2: Initialize and print the Dataset. six import StringIO from IPython. Usually, this involves a “yes” or “no” outcome. from sklearn. Please don't convert strings to numbers and use in decision trees. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. model = RandomForestClassifier(n_estimators=100, random_state=0) visualize_classifier(model, X, y); Jul 16, 2022 · We will first give you a quick overview of what is a decision tree to help you refresh the concept. Here, we will work with the sklearn’s wine dataset to look into tuning hyperparameters for our model. To make the rules look more readable, use the feature_names argument and pass a list of your feature names. 5 use Entropy. You'll also learn the math behind splitting the nodes. Cross-validate your model using k-fold cross validation. #. Though the Decision Tree classifier is one of the most sophisticated classification algorithms, it may have certain limitations, especially in real-world scenarios. max_depth , min_samples_leaf , etc. The core principle of AdaBoost (Adaptive Boosting) is to fit a sequence of weak learners (e. model_selection. Some of its deterrents are as mentioned below: Decision Tree Classifiers often tend to overfit the training data. Python3. get_depth Return the depth of the decision tree. See here, a decision tree classifying the Iris dataset according to continuous values from their columns. g. A decision tree is a hierarchical structure that uses a series of binary decisions to classify instances. However, this comes at the price of losing data which may be valuable (even though incomplete). Apr 26, 2021 · Gradient boosting refers to a class of ensemble machine learning algorithms that can be used for classification or regression predictive modeling problems. tree A decision tree classifier. First, three exemplary classifiers are initialized ( DecisionTreeClassifier , KNeighborsClassifier, and SVC) and Nov 16, 2023 · Scikit-Learn, or "sklearn", is a machine learning library created for Python, intended to expedite machine learning tasks by making it easier to implement machine learning algorithms. It can be used with both continuous and categorical output variables. , Manifold learning- Introduction, Isomap, Locally Linear Embedding, Modified Locally Linear Embedding, Hessian Eige 4 days ago · In Python, grid search is performed using the scikit-learn library’s sklearn. tree import DecisionTreeClassifier from Scikit-Learn. After I use class_weight='balanced', the record Jun 22, 2020 · Decision Tree learning is a process of finding the optimal rules in each internal tree node according to the selected metric. Q2. A non zero element of. Predictions are made by calculating the prediction for each decision tree, then taking the most popular result. 21: 'drop' is accepted. algorithm {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’ Algorithm used to compute the nearest neighbors: ‘ball_tree’ will use BallTree ‘kd_tree’ will use KDTree Export a decision tree in DOT format. My question is in the code below, the cross validation splits the data, which i then use for both training and testing. Each decision tree is like an expert, providing its opinion on how to classify the data. Cross-validation: evaluating estimator performance #. multiclass. This plot compares the decision surfaces learned by a decision tree classifier (first column), by a random forest classifier (second column), by an extra- trees A Decision Tree is a supervised Machine learning algorithm. The decision trees is used to fit a sine curve with addition noisy observation. Key concepts such as root nodes, decision nodes, leaf nodes, branches, pruning, and parent-child node Return the decision path in the tree. float32 and if a sparse matrix is provided to a sparse csc_matrix. Example of Precision-Recall metric to evaluate classifier output quality. It structures decisions based on input data, making it suitable for both classification and regression tasks. 299 boosts (300 decision trees) is compared with a single decision tree regressor. ensemble import RandomForestClassifier. Examples. See full list on datagy. [ ] from sklearn. Invoking the fit method on the VotingClassifier will fit clones of those original estimators that will be stored in the class attribute self. See the glossary entry on imputation. import numpy as np . (Image by author) Decision trees are robust in terms of the data types they can handle, but the algorithm itself is not very robust. Based on this, the model will define the importance of each feature for the classification. Decision-Tree uses tree-splitting criteria for splitting the nodes into sub-nodes until Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. Changed in version 0. This is highly misleading. This might include the utility, outcomes, and input costs, that uses a flowchart-like tree structure. Algorithm. To determine the best parameters (criterion of split and maximum Build a classification decision tree. extractParamMap(extra:Optional[ParamMap]=None) → ParamMap ¶. The class allows you to: Apply a grid search to an array of hyper-parameters, and. Notes The default values for the parameters controlling the size of the trees (e. Attributes: classes_ : array of shape = [n_classes] or a list of such arrays. The good thing about the Decision Tree Classifier from scikit-learn is that the target variable can be categorical or numerical. StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0) [source] #. Beside factor, the two main parameters that influence the behaviour of a successive halving search are the min_resources parameter, and the number of candidates (or parameter combinations) that are evaluated. We can import DT classifier as from sklearn. estimators_. An example of a decision tree is a flowchart that helps a person decide what to wear based on the weather conditions. Stack of estimators with a final classifier. So I convert this column to be of type category like this: 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. X {array-like, sparse matrix, dataframe} of shape (n_samples, 2) Input data that should be only 2-dimensional. We will compare their accuracy on test data. For instance, you can see X[3] < 0. Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. RandomForestClassifier. It is used in machine learning for classification and regression tasks. A single decision tree is the classic example of a type of classifier known as a white box. node_indicator = estimator. Introduction to Decision Trees. The results of each test determine the next test to be applied, and ultimately, the label value to be predicted for the observations. DecisionTreeClassifier(class_weight={A:9,B:1}) The class_weight='balanced' will also work, It just automatically adjusts weights according to the proportion of each class frequencies. Decision trees, non-parametric supervised learning algorithms, are explored from basics to in-depth coding practices. They can be used for the classification and regression tasks. The advantages of support vector machines are: Effective in high dimensional spaces. tree import DecisionTreeClassifier from sklearn. 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. In this article, we will see the tutorial for implementing random forest classifier using the Sklearn (a. Course. display import Image from sklearn. class sklearn. plot_tree method (matplotlib needed) plot with sklearn. Neural network models (unsupervised) 2. Mar 15, 2024 · A decision tree in machine learning is a versatile, interpretable algorithm used for predictive modelling. predict (X[, check_input]) Nov 16, 2020 · Here, we will use the iris dataset from the sklearn datasets databases which is quite simple and works as a showcase for how to implement a decision tree classifier. Build a decision tree classifier from the training set (X, y). import matplotlib. This dataset is very small, with only a 150 samples. Supported strategies are “best” to choose the best split and “random” to choose the best random split. Examples concerning the sklearn. Note. 5 and maximum purity is 0, whereas Entropy has a maximum impurity of 1 and maximum purity is 0. tree import export_text. sklearn. Returns the documentation of all params with their optionally default values and user-supplied values. Jan 10, 2023 · In a multiclass classification, we train a classifier using our training data and use this classifier for classifying new examples. Greater values of ccp_alpha increase the number of nodes pruned. It can be utilized in various domains such as credit, insurance, marketing, and sales. In this lesson, we will focus on using decision trees for classification. Jan 11, 2023 · Here, continuous values are predicted with the help of a decision tree regression model. . Support Vector Machines #. The treatment of categorical data becomes crucial during the tree May 15, 2019 · For instance, in AdaBoost, the decision trees have a depth of 1 (i. Gradient boosting is also known as gradient tree boosting, stochastic gradient boosting (an extension), and gradient boosting machines, or GBM for short. DataFrame. Post pruning decision trees with cost complexity pruning. ) lead to fully grown and unpruned trees which can potentially be very large on some data sets. , to infer them from the known part of the data. Classification trees determine whether an event happened or didn’t happen. tree import export_graphviz import pydotplus import graphviz # DOT data dot_data = tree. This tutorial will cover the concept, workflow, and examples of the k-nearest neighbors (kNN) algorithm. Remember, decision trees are prone to overfitting. Precision-Recall #. Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier. In scikit-learn it is DecisionTreeClassifier. Number of grid points to use for plotting Parameters: estimatorslist of (str, estimator) tuples. The Gini index has a maximum impurity is 0. Plot the decision surface of decision trees trained on the iris dataset. Parameters. You can run the code in sequence, for better understanding. 4. Decision Trees. 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. From installation to creating DMatrix and building a classifier, this tutorial covers all the key aspects. This Feb 2, 2010 · Density Estimation: Histograms. Multi-output Decision Tree Regression. But regarding this question, in iris you have three classes (Setosa, Versicolour, and Virginica). Nov 29, 2023 · Their respective roles are to “classify” and to “predict. Multilabel classification. Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string. OneVsRestClassifier. Aim of this article – We will use different multiclass classification methods such as, KNN, Decision trees, SVM, etc. In addition, the predictions made by each decision tree have varying impact on the final prediction made by the model. We use a random set of 130 for training and 20 for testing the models. export_graphviz method (graphviz needed) plot with dtreeviz package (dtreeviz and graphviz needed) Attempting to create a decision tree with cross validation using sklearn and panads. OneVsOneClassifier# OneVsOneClassifier constructs one classifier per pair of classes. A decision tree consists of the root nodes, children nodes Sep 22, 2021 · Introduction. Jul 1, 2018 · The decision_path. The predictions made by a white box classifier can easily be understood. This function generates a GraphViz representation of the decision tree, which is then written into out_file. Understanding the decision tree structure. A basic strategy to use incomplete datasets is to discard entire rows and/or columns containing missing values. The next video will show you how to code a decisi Oct 27, 2021 · Limitations of Decision Tree Algorithm. Apr 27, 2016 · I am training an sklearn. tree module. Boosting algorithms such as AdaBoost, Gradient Boosting, and XGBoost are widely used machine learning algorithm to win the data science competitions. Each internal node corresponds to a test on an attribute, each branch Jan 5, 2022 · A random forest classifier is what’s known as an ensemble algorithm. It splits data into branches like these till it achieves a threshold value. import pandas as pd . We will perform all this with sci-kit learn Precision-Recall — scikit-learn 1. ensemble. In a region of feature space represented by the node of a decision tree, recall that the "impurity" of the region is measured by quantifying the inhomogeneity, using the probability of the class in that region. Some of the columns of this data frame are strings that really should be categorical. It has easy-to-use functions to assist with splitting data into training and testing sets, as well as training a model, making predictions, and evaluating the model. # method allows to retrieve the node indicator functions. An estimator can be set to 'drop' using set_params. Comparison between grid search and successive halving. Kernel Density Estimation. Restricted Boltzmann machines. tree. Classification trees. Discover the power of XGBoost, one of the most popular machine learning frameworks among data scientists, with this step-by-step tutorial in Python. y array-like of shape (n_samples,) or (n_samples, n_outputs) Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. For example, CART uses Gini; ID3 and C4. The parameters of the estimator used to apply these methods are optimized by cross-validated 1. We will first cover an overview of what is random forest and how it works and then implement an end-to-end project with a dataset to show an example of Sklean random forest with May 17, 2024 · A decision tree is a flowchart-like structure used to make decisions or predictions. In a random forest classification, multiple decision trees are created using different random subsets of the data and features. For each classifier, the class is fitted against all the other classes. May 14, 2024 · You might have already learned how to build a Decision-Tree Classifier, but might be wondering how the scikit-learn actually does that. # indicator matrix at the position (i, j) indicates that the sample i goes. A decision tree is boosted using the AdaBoost. Dec 27, 2019 · Applying Decision Tree Classifier: Next, I created a pipeline of StandardScaler (standardize the features) and DT Classifier (see a note below regarding Standardization of features). explainParams() → str ¶. For clarity purpose, given the iris dataset, I A decision tree is a classifier which uses a sequence of verbose rules (like a>7) which can be easily understood. compute_node_depths() method computes the depth of each node in the tree. a Scikit Learn) library of Python. The power hidden in the forest Aug 24, 2016 · I edited and undelete my previous answer. Apr 27, 2020 · In this case, you can pass a dic {A:9,B:1} to the model to specify the weight of each class, like. tree import Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. Nov 2, 2022 · There seems to be no one preferred approach by different Decision Tree algorithms. inspection module provides a convenience function from_estimator to create one-way and two-way partial dependence plots. 373K. 5. Internally, it will be converted to dtype=np. ” Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. grid_resolution int, default=100. May 31, 2024 · A. semi_supervised are able to make use of this additional unlabeled data to better capture the shape of the underlying data distribution and generalize better to new samples. 4 hr. Dec 8, 2019 · A decision tree will find the optimal splitting point for all attributes, often reusing attributes multiple times. ”. Decision trees, being a non-linear model, can handle both numerical and categorical features. Once you've fit your model, you just need two lines of code. A meta-estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. Parameters: X {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. How does a prediction get made in Decision Trees Decision Tree Regression with AdaBoost #. 2 leaves). Jul 14, 2022 · Lastly, let’s now try visualizing the decision tree classifier model. 3. Let’s see the Step-by-Step implementation –. Scikit-Learn provides plot_tree () that allows us Plot the decision surfaces of ensembles of trees on the iris dataset# Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. The sklearn. The branches depend on a number of factors. The default one is gini but you can also use entropy. Example: The wine dataset using a "gini" criterion has a feature importance of: For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. pyplot as plt. In the proceeding example, we’ll be using a dataset that categories people as attractive or not based on certain features. As the number of boosts is increased the regressor can fit more detail. The decision tree is like a tree with nodes. Gaussian mixture models- Gaussian Mixture, Variational Bayesian Gaussian Mixture. For the sake of simplicity, we focus the discussion on the hyperparamter max_depth, which controls the maximal depth of the decision tree. Thus, simply replacing the strings with a hash code should be avoided, because being considered as a continuous numerical feature any coding you will use will induce an order which simply does Aug 23, 2023 · Building the Decision Tree; Handling Overfitting; Making Predictions; Conclusion; 1. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al [ 1 ] . We can see that if the maximum depth of the tree (controlled by the max_depth parameter) is set too high, the decision trees learn too fine details of Histogram-based Gradient Boosting Classification Tree. The main goal of DTs is to create a model predicting target variable value by learning simple 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. The semi-supervised estimators in sklearn. arange(3, 15)} # decision tree model dtree_model=DecisionTreeClassifier() #use gridsearch to test all class sklearn. This tutorial won’t go into the details of k-fold cross validation. Trained estimator used to plot the decision boundary. Inherently tree based algorithms in sklearn interpret one-hot encoded (binarized) target labels as a multi-label problem. 1 documentation. For instance, in the example below Jun 10, 2020 · Here is the code for decision tree Grid Search. Here are a few examples to help contextualize how decision A decision tree classifier. get_n_leaves Return the number of leaves of the decision tree. e. 3. 12. A 1D regression with decision tree. There is a Github issue on this ( #4899) from June 2015, but it is still open (UPDATE: it is now closed, but continued in #12866, so the issue is still not resolved). Feb 23, 2019 · Figure-1) Our decision tree: In this case, nodes are colored in white, while leaves are colored in orange, green, and purple. Jun 28, 2021 · Example of Decision trees with high bias and high variance. The strategy used to choose the split at each node. Bennett, “Decision Tree Construction Via Linear Programming. Still effective in cases where number of dimensions is greater than the number of samples. So, in this article, we will cover this in a step-by-step manner. Read more in the User Guide. That task could be accomplished with a Decision Tree, a type of classifier in Scikit-Learn. export_text method; plot with sklearn. Parameters: criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. GridSearchCV function. model_selection import GridSearchCV def dtree_grid_search(X,y,nfolds): #create a dictionary of all values we want to test param_grid = { 'criterion':['gini','entropy'],'max_depth': np. decision_path(X_test) # Similarly, we can also have the leaves ids reached by each sample. As a result, it learns local linear regressions approximating the sine curve. Since decision trees are very intuitive, it helps a lot to visualize them. Precision-Recall is a useful measure of success of prediction when the classes are very imbalanced. Successive Halving Iterations. One option is to use the decision tree classifier in Spark - in which you can explicitly declare the categorical features and their ordinality. This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. get_params ([deep]) Get parameters for this estimator. To get the most from this tutorial, you should have basic OneVsRestClassifier #. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical Jan 13, 2021 · Here, I've explained Decision Trees in great detail. First, import export_text: from sklearn. Decision Tree Regression. However, you can remove this problem by simply planting more trees! Oct 15, 2017 · During all the explaination, I'll use the wine dataset example: Criterion: It is used to evaluate the feature importance. In this notebook we illustrate decision trees in a multiclass classification problem by using the penguins dataset with 2 features and 3 classes. We will explain this process in more detail as we consider examples. The decision trees implemented in scikit-learn uses only numerical features and these features are interpreted always as continuous numeric variables. Jun 3, 2020 · In this exercise, you'll train a classification tree on the Wisconsin Breast Cancer dataset using entropy as an information criterion. tree_ also stores the entire binary tree structure, represented as a Jan 26, 2019 · There are 4 methods which I'm aware of for plotting the scikit-learn decision tree: print the text representation of the tree with sklearn. 8. Decision Trees) on repeatedly re-sampled versions of the data. fit (X, y[, sample_weight, check_input, …]) Build a decision tree classifier from the training set (X, y). 14. A node may have zero children (a terminal node), one child (one side makes a prediction directly) or two child nodes. 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. splitter{“best”, “random”}, default=”best”. For example, 'Color' is one such column and has values such as 'black', 'white', 'red', and so on. Support vector machines (SVMs) are a set of supervised learning methods used for classification , regression and outliers detection. Nov 6, 2020 · And, in the end plot the tree: from sklearn. 2. In the below example we show how to create a grid of partial dependence plots: two one-way PDPs for the features 0 and 1 and a two-way PDP between the two features: Jul 21, 2018 · There is a key difference in all these implementation which are being ignored. 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. clf = tree. 1. A better strategy is to impute the missing values, i. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical Feb 21, 2023 · A decision tree is a decision model and all of the possible outcomes that decision trees might hold. The function to measure the quality of a split. Semi-supervised learning is a situation in which in your training data some of the samples are not labeled. k. A tree can be seen as a piecewise constant approximation. io Iris classification with scikit-learn. R2 [ 1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. New nodes added to an existing node are called child nodes. It is used in both classification and regression algorithms. tree import DecisionTreeClassifier. Once exported, graphical renderings can be generated using, for example: The sample counts that are shown are weighted with any sample_weights that might be present. Parameters: estimator object. Source(dot Feb 9, 2022 · The GridSearchCVclass in Sklearn serves a dual purpose in tuning your model. Step 1: Import the required libraries. core. jp rb jf xe tj pu ww yj xf ib