How to calculate error rate in decision tree python. Let’s get started.

How to calculate error rate in decision tree python Dec 14, 2023 · A decision tree expressing attribute tests as nodes and class labels as leaves is the end product. Provide details and share your research! But avoid …. Nov 12, 2020 · Here for the above equation, j and s are found such that this equation has the minimum value. We then used the Nov 16, 2019 · In my most recent blog, I discussed the two most common metrics in decision trees, the entropy/information gain and the Gini index. The major hyperparameters that are used to fine-tune the decision: Criteria : The quality of the split in the decision tree is measured by the function called Aug 16, 2019 · It appears that you are training your model and generating predictions on the same dataset (X_new). Every policy is 1 row. In this example, we will use the social network ads data concerning the Gender, Age, and Estimated Salary of several Dec 14, 2023 · A decision tree expressing attribute tests as nodes and class labels as leaves is the end product. ix[:,:2], df. py: A script demonstrating how to train and evaluate a decision tree using a dataset (e. Mar 18, 2020 · Post pruning a Decision tree as the name suggests ‘prunes’ the tree after it has fully grown. You can build a completely custom scorer object from a simple python function using make_scorer, which can take several parameters: the python function you want to use (my_custom_loss_func in the example below) whether the python function returns a score (greater_is_better=True, the default) or a loss (greater_is_better=False). iloc[:,1:2]. Oct 19, 2020 · Suppose we have a decision tree output from python that looks like this: Sample Decision Tree Output Suppose we are interested in predicting the class 1 of the target variable (aka the predicted event probability is the Prob (class = 1)). They're useful for prefix sums, which is what I think you're looking for. 1, apply method is implemented in clf. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. drop(['class', 'who', 'adult_male', 'deck', ' Aug 1, 2020 · Kick-start your project with my new book Imbalanced Classification with Python, including step-by-step tutorials and the Python source code files for all examples. scale(X. • The greedy procedure will be effective on the data that we are given, where effective- I would use it before the parameter search to give a vague idea of the tree settings to search (I try a few of my grid search settings and produce this plot). The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Returns: self. e. Samples have equal weight when sample_weight is not provided. 1. You should not attempt to evaluate your model's performance using this output - because you are applying the model to the same data you trained it on, your evaluation will be over-optimistic. Apr 10, 2019 · I am working on Decision Tree model . tree import DecisionTreeClassifier from sklearn. The final decision tree can explain exactly why a specific prediction was made, making it very attractive for operational use. DataFrame({'col1':[0,1,2,3],'col2':[3,4,5,6],'dv':[0,1,0,1]}) # create decision tree dt = DecisionTreeClassifier(max_depth=5, min_samples_leaf=1) dt. iloc[:,2]. Unlike traditional linear regression, which assumes a straight-line relationship between input features and the target variable, Decision Tree Regression is a non-linear re This is highly misleading. plotly Aug 26, 2020 · The performance of a machine learning model can be characterized in terms of the bias and the variance of the model. This project analyzes performance by calculating the May 6, 2022 · import matplotlib. In addition, we will include the different hyperparameters that a decision tree generally offers. Apr 5, 2013 · Another option is to calculate the confusion matrix, which tells you the accuracy of both classes and the alpha and beta errors: from sklearn. I am trying to find out which are these misclassified instances and in which leaf Attempting to create a decision tree with cross validation using sklearn and panads. Learn how it evaluates machine-generated sentences against human references in text summarization, translation, and more with NLTK in Python. It is typically used within Decision Trees. To calculate the Gini index in a decision tree, follow these steps: Calculate Gini Impurity for Each Node:For a node t containing Nt data points, calculate the Gini imp Feb 3, 2019 · I am training a decision tree with sklearn. ] it will look like [0. Oct 3, 2020 · Decision tree is one of the well known and powerful supervised machine learning algorithms that can be used for classification and regression problems. x Feb 28, 2021 · In Python using SkLearn, you could use the following to create and receive a score on a Decision Tree: tr = tree. It works for both continuous as well as categorical output variables. Currently for my model I don't provide max_depth so it should take the value that has maximum leaves. It can handle both classification and regression tasks. Jan 22, 2017 · Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. load_dataset('titanic') titanic = titanic. They are popular because the final model is so easy to understand by practitioners and domain experts alike. Apr 16, 2024 · Types of Hyperparameters in Decision Tree. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket Press Copyright Jul 31, 2017 · I've been following the ML course by Tom Mitchel and in Decision Tree (DT) Learning, the Entropy Gain is chosen as ruling criterion for the choice of a feature/parameter x_i as child of another fea Pruning decision trees is akin to sculpting a masterpiece. For example, if there is a class imbalance ratio of 20:80 (imbalanced data), then the recall score will be more useful than accuracy because it can provide information about how well the machine learning model identified rarer events. Construct a confusion matrix. To calculate the expected value in a decision tree, follow these steps: To calculate the expected value in a decision tree, follow these steps: Identify Possible Oct 3, 2020 · Decision tree is one of the well known and powerful supervised machine learning algorithms that can be used for classification and regression problems. Decision Tree Regression; Plot the decision surface of decision trees trained on the iris dataset; Post pruning decision trees with cost complexity pruning; Understanding the decision tree structure; Decomposition. best_error[i] holds the entropy of the i-th node splitting on feature DecisionTreeClassifier. I will be attempting to find the best depth of the tree by recreating it n times with different max depths set. We'll plot feature importance obtained from the Decision Tree model to see which features have the greatest predictive power. This is a dataset of data that the model has not been trained on. Unlike traditional linear regression, which assumes a straight-line relationship between input features and the target variable, Decision Tree Regression is a non-linear re Mar 13, 2021 · Plotly can plot tree diagrams using igraph. values #Creating a model object and fiting the data reg = DecisionTreeRegressor(random_state=0) reg. 7 on Windows, what is wrong with my code to calculate AUC? Thanks. 25) using the given feature as the target # TODO: Set a random state. model_selection import train_test_split # Import train_test_split function from sklearn import metrics #Import scikit-learn metrics module for Aug 24, 2016 · Using scikit-learn with Python 2. It involves a delicate balance between accuracy and simplicity. 25 0. Decision trees also provide the foundation for […] Nov 25, 2024 · Answer: To calculate expected value in a decision tree, multiply the outcome values by their respective probabilities and sum the results. columns = ['X1', 'X2', 'X3', 'X4', 'Y Apr 25, 2020 · @xdurch0 I kindly suggest we avoid convoluting an ultra-simple question about very basic definitions from an obvious beginner. i. Returns: routing MetadataRequest Jan 21, 2024 · We will train a model using Python, calculate metrics to determine the generalization error, and identify the errors as bias or variance. Example. A 35 year old male with 10 (year?) of education would be mapped to leaf-node 7. datasets import load_iris from sklearn. The deeper the tree, the more complex the decision rules and the fitter the model. Hyperparameter in decision trees are essential settings that controls the behavior and the structure of the model during the training phase. 003. figure(figsize=(10, 6)) plot_tree(decision_tree=model_dt, feature_names=explanatory. drop(['Frozen'], axis = 1) # TODO: Split the data into training and testing sets(0. Feb 13, 2024 · Answer: To calculate the Gini index in a decision tree, compute the sum of squared probabilities of each class subtracted from one. We already have all the ingredients to calculate our decision tree. To calculate information gain in a decision tree, follow these steps: Calculate the Entropy of the Parent Node:Compute the entropy of the parent node usi There is a much simpler way than the answer, if I had the time to write the demo code: Fenwick trees. 5%. 2) Target function is discrete-valued Feb 19, 2021 · This is confusion matrix where on the left side, we have the actual values and on the top side, we have the predicted values, these values can be interchanged also. DecisionTreeClassifier() the max_depth parameter defaults to None. Let us read the different aspects of the decision tree: Rank. Please check User Guide on how the routing mechanism works. Mar 25, 2022 · Misclassification Rate = (70 + 40) / (400) Misclassification Rate = 0. This same approach can be used for ensembles of decision trees, such as the random forest and stochastic gradient boosting min_weight_fraction_leaf float, default=0. This means the model incorrectly predicted the outcome for 27. values y =df. , Breast Cancer dataset). metrics import accuracy_score import numpy as np import pylab as pl features_train, labels_train, features_test, labels_test = makeTerrainData() X = features_train Y = labels_train clf Mar 12, 2012 · Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. CLASSIFICATION ERROR RATES IN DECISION TREE EXECUTION Laviniu Aurelian Badulescu University of Craiova, Faculty of Automation, Computers and Electronics, Jul 15, 2015 · From my experience I could recommend logloss or MSE (or just mean squared error). The example below is intended to be run in a Jupyter notebook. Nov 7, 2020 · Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Sep 10, 2017 · I am trying to evaluate a relevance of features and I am using DecisionTreeRegressor(). pyplot as plt plt. This project analyzes performance by calculating the Return the depth of the decision tree. 02605 where as when I run the model on training set came as 0. In this case, you can pass a dic {A:9,B:1} to the model to specify the weight of each class, like DTR will sort of create a partition level for all the values Check the graph - Click here from sklearn. use the larger value attribute from each node. One popular decision tree method that is well-known for its accuracy, efficiency, and capacity to handle both continuous and categorical characteristics is the C5 algorithm. DecisionTreeClassifier(random_state=rseed, min_samples_split=2, ccp_alpha=0. Decision trees also provide the foundation for […] Attempting to create a decision tree with cross validation using sklearn and panads. The Advantages and Disadvantages of the C5 algorithm. However, you can set max_leaf_nodes to MaxNumSplits + 1 and the result should be equivalent. Jan 12, 2022 · # importing decision tree algorithm from sklearn. Let's first load the required libraries. Holds the lower bounds of the x and y coordinates. toarray()) #I generate a KFold in order to make cross validation kf = KFold(len(X), n_folds=10, indices=True, shuffle=True, random_state=1) #I start the cross Jan 20, 2018 · Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. Nov 13, 2017 · the answer in my top is correct, you are getting binary output because your tree is complete and not truncate in order to make your tree weaker, you can use max_depth to a lower depth so probability won't be like [0. columns, filled= True); How does the Decision Tree Algorithm computes the Mathematical Equation? The Decision Tree and the Linear Regression algorithms look for the best numbers in a mathematical equation. It was suggested to use a decision tree, which I've already built. 85] another problem here is that the dataset is very small and easy to solve so better to use a more complex dataset some link that might make this May 14, 2024 · Decision Tree is one of the most powerful and popular algorithms. Nov 2, 2024 · CART( Classification And Regression Trees) is a variation of the decision tree algorithm. 26315789473685" The problem about zero accuracies is solved, Thx! python-3. @CihanCeyhan - I read the documentation. Members ----- lowerBounds : List of Float of size 2. metrics import confusion_matrix con_mat = confusion_matrix(true_values, pred_values, [0, 1]) In case your labels are 0 and 1. When I use: dt_clf = tree. May 6, 2022 · import matplotlib. fit(X,y) # Visualising the Decision Tree Regression results (higher resolution) X_grid = np Decision Trees. 0. Here is one solution based on my correspondence message in the scikit-learn mailing list: . upperBounds : List of Float of size 2. Blind source separation using FastICA; Comparison of LDA and PCA 2D projection of Iris dataset; Faces dataset Jan 9, 2025 · Answer: To calculate information gain in a decision tree, subtract the weighted average entropy of child nodes from the entropy of the parent node. To calculate the Gini index in a decision tree, follow these steps: Calculate Gini Impurity for Each Node:For a node t containing Nt data points, calculate the Gini imp May 2, 2024 · Step 3: Visualization of Accuracy and Recall . While the confusion matrix tells me how many misclassifications have occurred It does not exactly tell me which particular instances in X_train have been misclassified. However, your code is not robust, because we've no guarantee that the list of coordinates actually includes a point lying exactly on the EER line. The regions R1, R2 are selected based on that value of s and j such that the equation above has the Aug 28, 2024 · Recall score can be used in the scenario where the labels are not equally divided among classes. Dec 11, 2019 · Decision trees are a powerful prediction method and extremely popular. Asking for help, clarification, or responding to other answers. class_probabilitiesDec = clf. If a loss, the Jul 15, 2024 · When it comes to predicting continuous values, Decision Tree Regression is a powerful and intuitive machine learning technique. My name is Zach Bobbitt. For instance, in the example below, decision trees learn from data to approximate a sine curve with a set of if-then-else decision rules. cross_validation import cross_val_score from In this tutorial, we are are going to evaluate the performance of a data set through Decision Tree Regression in Python using scikit-learn machine learning library. The train_and_evaluate() function is called for each maximum depth, and the accuracy and recall scores along with the trained classifiers are stored for further analysis. 005) How to train a decision tree in Python from scratch Determining the depth of the tree. Apr 27, 2015 · Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. This can be done using a Python library such as Scikit-learn. A model with high variance is […] Nov 25, 2024 · Decision Tree is one of the most powerful and popular algorithms. Now, we must create a function that, given a mask, makes us a split. Rank <= 6. Nov 4, 2019 · Calculating the error rate for a decision tree model in R involves evaluating the model's predictions against actual outcomes from a test dataset. May 23, 2015 · I finally got it to work. There is no way to handle categorical data in scikit-learn. The art lies in recognizing that a tree can become overgrown with Sep 13, 2018 · class Box: ''' Class to keep track of the xy-rectangle that a node in a decision tree classifier applies to. If you want the entropy of all examples that reach the i-th node look at DecisionTreeClassifier. Mar 11, 2024 · Decision trees are a popular machine learning algorithm used for both classification and regression tasks. A Python implementation of the Decision Tree algorithm, focusing on classification and error rate evaluation. 275 or 27. 5 means that every comedian with a rank of 6. predict_proba(X_test) Nov 21, 2024 · Answer: To calculate the Gini index in a decision tree, compute the sum of squared probabilities of each class subtracted from one. One option is to use the decision tree classifier in Spark - in which you can explicitly declare the categorical features and their ordinality. feature[i]. Mar 8, 2018 · Similarly clf. 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. tree import DecisionTreeRegressor #Getting X and y variable X = df. Nov 18, 2024 · This can be done using any machine learning algorithm, such as logistic regression, decision tree, or random forest. dv) May 23, 2015 · I finally got it to work. Jan 24, 2018 · Accuracy: The number of correct predictions made divided by the total number of predictions made. get_metadata_routing [source] # Get metadata routing of this object. Oct 26, 2022 · To calculate loss, we need to define a suitable loss function. Let’s compare entropy and misclassification loss with the help of an example. read_csv('iris. dv) Mar 16, 2021 · I am trying to create a decision tree for a dataset and study the resulting confusion matrix. fit_transform(trainList) #I scale the matrix (don't know why but without it, it makes an error) X=preprocessing. Decision tree algorithms like classification and regression trees (CART) offer importance scores based on the reduction in the criterion used to select split points, like Gini or entropy. predict_proba predicts the probability for each label from your X Data, thus:. The opposite of misclassification rate would be accuracy, which is calculated as: Accuracy = 1 – Misclassification rate Mar 10, 2018 · import sys from class_vis import prettyPicture from prep_terrain_data import makeTerrainData from sklearn. In this article, We are going to implement a Decision tree in Python algo Oct 26, 2022 · Cookie Duration Description; cookielawinfo-checkbox-analytics: 11 months: This cookie is set by GDPR Cookie Consent plugin. weighted_n_node_samples to get the gini/entropy value and number of samples at the each node & at it's children. The depth of a tree is the maximum distance between the root and any leaf. In this post, I will discuss how to use Python to code a Mar 29, 2020 · Decision Tree Feature Importance. Apr 10, 2024 · Decision Tree is one of the most powerful and popular algorithms. Feb 9, 2023 · Image Source: Jeremy Jordan Implement Decision Tree Classification in Python. The policies that run for more days have to have higher weight compared to the policies that run for less days. • The class label can be predicted using a logical set of decisions that can be summarized by the decision tree. Fixed typos about what precision and recall seek to minimize (thanks for the May 29, 2019 · You cannot predict the probability of your labels. May 2, 2024 · Step 3: Visualization of Accuracy and Recall . It removes a sub-tree and replaces it with a leaf node, the most frequent class of the sub-tree May 5, 2021 · import numpy as np import pandas as pd import seaborn as sns import matplotlib. After scikit-learn version 0. If you want a nice output, you can add this code: May 6, 2013 · The attribute DecisionTreeClassifier. 275; The misclassification rate for this model is 0. Please don't convert strings to numbers and use in decision trees. decision_tree_zhoumath_example_script. Oct 23, 2023 · A decision tree assigns one prediction (in your case "Yes" or "No") to each leaf-node (in your case this would be Nodes 2, 4, 7, 8). impurity & clf. model_selection import train_test_split # Import train_test_split function from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation #split dataset in features and Decision Trees. g. A model with high bias makes strong assumptions about the form of the unknown underlying function that maps inputs to outputs in the dataset, such as linear regression. Scikit-Learn uses the Classification And Regression Tree (CART) algorithm to train Decision Trees (also called “growing†trees). We're going to predict the majority class associated with a particular node as True. Jan 15, 2025 · Python | Decision Tree Regression using sklearn When it comes to predicting continuous values, Decision Tree Regression is a powerful and intuitive machine learning technique. Then once, the optimum parameters are found, I do it once more to check if I cant eek out any more performance by growing the forest a bit larger. I have 80% data in training set and 20% test set. Make predictions on a test dataset. feature for left & right children. Blind source separation using FastICA; Comparison of LDA and PCA 2D projection of Iris dataset; Faces dataset Oct 2, 2020 · Hey there. The maximum depth of the tree. Dec 17, 2019 · In the generated decision tree regression model, there is an MSE attribute when using graphviz to view the tree structure. I created my own function to extract the rules from the decision trees created by sklearn: import pandas as pd import numpy as np from sklearn. To calculate the Gini index in a decision tree, follow these steps: Calculate Gini Impurity for Each Node:For a node t containing Nt data points, calculate the Gini imp May 26, 2019 · print('Accuracy tree is', r2_score(y_test, tree_pred)*100) # output is "Accuracy tree is 65. tree import DecisionTreeClassifier # entropy means information gain classifer = DecisionTreeClassifier(criterion='entropy', random_state=0) # providing the training dataset classifer. tree_, therefore, I followed the following steps: The decision tree uses your earlier decisions to calculate the odds for you to wanting to go see a comedian or not. More specifically, the Gini Impurity is used when training/growing a decision tree on a labelled training set. Python Decision-tree algorithm falls under the category of supervised learning algorithms. 16. • The greedy procedure will be effective on the data that we are given, where effective- Dec 29, 2016 · Thanks for contributing an answer to Cross Validated! Please be sure to answer the question. The major hyperparameters that are used to fine-tune the decision: Criteria : The quality of the split in the decision tree is measured by the function called Feb 9, 2023 · Image Source: Jeremy Jordan Implement Decision Tree Classification in Python. In this article, We are going to implement a Decision tree in Python algo Jun 8, 2022 · I have a data set with policies that run for different numbers of days. In this article, We are going to implement a Decision tree in Python algo Jul 10, 2015 · #I transform the data from JSON form to a numerical one X=vec. Is there a way that I can do that in tree based models in python? Especially, decision tree model. The cookie is used to store the user consent for the cookies in the category "Analytics". How to fix sklearn warnings? Just simply (as yangjie noticed) overwrite average parameter with one of these values: 'micro' (calculate metrics globally), 'macro' (calculate metrics for each label) or 'weighted' (same as macro but with auto weights). Some advantages of decision trees are: Aug 13, 2020 · I'm using a Dutch dataset with categorical variables: date of infection, fatality or cured, gender, age-group etc. Since I'm new to decision trees I would like some assistance. children_left/right gives the index to the clf. By default, the score computed at each CV iteration is the score method of the estimator. Deep-dive into the BLEU Score: A guide to understanding BLEU (Bilingual Evaluation Understudy), a vital metric in NLP. from sklearn. In this example, we will use the social network ads data concerning the Gender, Age, and Estimated Salary of several Aug 16, 2019 · It appears that you are training your model and generating predictions on the same dataset (X_new). pyplot as plt titanic = sns. init_error[i]. fit(df. Here we fetch the best estimator obtained from the gridsearchcv as the decision tree classifier Apr 5, 2018 · As far I know there is no option to limit the total number of splits (nodes) in scikit-learn. # Load libraries import pandas as pd from sklearn. Aug 6, 2024 · Answer: To calculate the Gini index in a decision tree, compute the sum of squared probabilities of each class subtracted from one. To test fitting, I want to assign max_depth value but need to know the maximum value that gets generated by default. 5 or lower will follow the True arrow (to the left), and the rest will follow the False arrow (to the right). Jun 27, 2024 · Decision Tree Classifier Building in Scikit-learn Importing Required Libraries. csv') df. metrics import confusion_matrix y_true = [1, -1, 0, 0, 1, -1 Mar 15, 2018 · . Using the above traverse the tree & use the same indices in clf. Jun 3, 2018 · Using your data, you can get all the metrics for all the classes at once: import numpy as np from sklearn. tree_. It is possible to change this by using the scoring parameter: A tree can be seen as a piecewise constant approximation. Let’s get started. Sep 9, 2020 · 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. The dataset is related to cars. Can be used to accurately and precisely draw the output of a decision tree classifier. According to the documentation, if max_depth is None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. You can use it offline these days too. Each sample is then mapped to exactly one leaf node and the prediction of that node is used. To calculate the Gini index in a decision tree, follow these steps: Calculate Gini Impurity for Each Node:For a node t containing Nt data points, calculate the Gini imp Oct 29, 2024 · A Python implementation of the Decision Tree algorithm, focusing on classification and error rate evaluation. I would like to have the prediction (target variable) expressed in a probability (%), not in a binary Jan 2, 2020 · Decision Tree is most effective if the problem characteristics look like the following points - 1) Instances can be described by attribute-value pairs. I need to obtain the MSE of each leaf node, and carry out subsequent opera Dec 25, 2018 · I try to predict in standard dataset "iris. fit(X_train,y_train) Notice that we have imported the Decision Tree Python sklearn module class. We will train two models: a simple linear regression model that may underfit and a complex decision tree that may overfit . Feb 5, 2015 · Your interpretation is correct - you're looking for the position at which TPR+FPR == 1. What you say, even if you recall correctly, is applicable to specific contexts only, and there is arguably a more appropriate time for such concerns in the future, should OP moves on from the (very) basics. 5% of the players. CART was first produced b Jan 27, 2025 · Visualizing the Decision Tree Classifier. Update Jan/2020: Improved language about the objective of precision and recall. Apr 27, 2020 · If the frequency of class A is 10% and the frequency of class B is 90%, then the class B will become the dominant class and your decision tree will become biased toward the classes that are dominant. import plotly. My question is in the code below, the cross validation splits the data, which i then use for both training and testing. Decision Tree visualization is used to interpret and comprehend model's choices. How do decision trees play a role in feature selection? Decision trees select the 'best' feature for splitting at each node based on information gain. What is Decision tree? A supervised learning method represented in the form of a graph where all possible solutions to a problem are checked. csv" import pandas as pd from sklearn import tree df = pd. 0289 , the difference between them is around 0. Oct 30, 2020 · I know that there is a built-in classifier in Python: from sklearn. The summary of the model ( based on training data) shows misclassification rate around 0. They model decisions based on the features of the data and their outcomes. decision_tree_zhoumath_examples/: Contains examples for using DecisionTreeZhoumath, including a script for training and evaluating the decision tree model. tree import DecisionTreeClassifier # Import Decision Tree Classifier from sklearn. The model is based on decision rules extracted from the training data. Aug 23, 2016 · From the user guide:. This metric can determine how best to split data at a given node in the tree, in order to create optimal child nodes. Analyze the confusion matrix. Thanks,. tree import DecisionTreeClassifier # dummy data: df = pd. max_depth int. Here the decision tree classifiers are trained with different maximum depths specified in the max_depths list. zqluu uzwyh mwyaerw noubgx ogxq zyhs mmfljpke yago fuxjxt cze pnpum oabbc dxgvl vwyklt rrafsls