How to print decision tree in python. The tutorial covers: Preparing the data.

it link here print-bst-tree. Now I am trying to plot it using pydot. Aug 27, 2021 · Decision trees in Python. The algorithm creates a model of decisions based on given data, which Jan 17, 2020 · Implement Decision Tree in Python using sklearn|Implementing decision tree in python#DecisionTreeInPython #DataSciencePython #UnfoldDataScienceHello,My name Feb 19, 2020 · This decision tree tutorial discusses how to build a decision tree model in Python. This section guides you through creating your first Decision Tree using Python, emphasizing practical experience and clarity. The answer should be scalable, such as if someone had 500 columns+ or more. Jul 17, 2021 · The main disadvantage of random forests is their lack of interpretability. predict(xtrain) fitted_tree. BaseEstimator. Jun 22, 2022 · CART (Classification and Regression Tree) uses the Gini method to create binary splits. Decision Trees #. Apr 9, 2014 · I need to print out a recursion tree as seen below. # method allows to retrieve the node indicator functions. estimators_: with open ('tree_' + str (i_tree) + '. The tree_. Follow the code to import the required packages in python. I prefer Jupyter Lab due to its interactive features. Aug 11, 2022 · Note: Remember, the goal here is to visualize our decision trees, thus any sort of split of the dataset in train and test set or other kinds of strategies to train the model will be executed. six import StringIO. _Booster. You can do that with networkx from sklearn. data) In this decision tree plot tutorial video, you will get a detailed idea of how to plot a decision tree using python. A non zero element of. resultados = pd. Display the top five rows from the data set using the head () function. Using the above traverse the tree & use the same indices in clf. The example gives the following output: The binary tree structure has 5 nodes and has the following tree structure: node=0 test node: go to node 1 if X[:, 3] <= 0. tree import DecisionTreeRegressor #Getting X and y variable X = df. savefig("temp. Oct 27, 2021 · How are Decision Trees used in Classification? The Decision Tree algorithm uses a data structure called a tree to predict the outcome of a particular problem. Moreover, when building each tree, the algorithm uses a random sampling of data points to train sklearn. Let’s change a couple of parameters to see if there is any effect on the accuracy and also to make the tree shorter. # Create Decision Tree classifier object. import numpy as np. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for 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. df = pandas. The example below is intended to be run in a Jupyter notebook. May 4, 2018 · You can find the decision rules as a dataframe through the function model. 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. Target01) dtreeviz expects the class_names to be a list or dict Jan 11, 2023 · Python | Decision Tree Regression using sklearn. datasets import make_classification. May 23, 2015 · I'm using decision tree classifier from the scikit-learn package in python 3. ix[:,"X0":"X33"] dtree = tree. getvalue()) 2) Or collect entire list in graph but just use first element to be sent to pdf. Jul 9, 2014 · I have trained a decision tree (Python dictionary) as below. Feb 20, 2018 · How to print the column names, such as feature 1, feature 2, feature 3, feature 4, feature 5 or feature 6 instead of -2 in the decision tree output. y_pred = clf. Mar 8, 2018 · Similarly clf. 4, and I want to get the corresponding leaf node id for each of my input data point. Key concepts such as root nodes, decision nodes, leaf nodes, branches, pruning, and parent-child node Feb 21, 2018 · 1. data, iris. import pandas as pd. tree_. tree. Now that we have the data, we can create and train our Decision tree model. node_indicator = estimator. LabelEncoder() label_encoder. Now let us see the python implementation of both Decision tree and Random forest models with the help of a telecom churn data set. See decision tree for more information on the estimator. X = data. I tried returning '', but then I get line unwanted extra line breaks. It is used in both classification and regression algorithms. I want this code to be general and work for other trees. //Decision Tree Python – Easy Tutorial. (graph, ) = pydot. PrintDt ( Tennis ) using this dictionary, for example: This is highly misleading. Decision trees for classification. import pydotplus. Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. clf = DecisionTreeClassifier(random_state=0) iris = load_iris() tree = clf. max_depth int. graph_from_dot_data(dot_data. import plotly. for i in range(1,372): Jul 27, 2019 · Therefore, we set a quarter of the data aside for testing. node=1 leaf node. Coding a regression tree I. Oct 26, 2020 · Step-1: Importing the packages. So your command should be: print (classification_report (y, y_pred)) But, second reason is that your y_pred is output of model. clone), or save the parameters for later evaluation. data, breast_cancer. compute_node_depths() method computes the depth of each node in the tree. export_graphviz (tree_in_forest, out_file = my_file) i_tree = i_tree + 1. You can use it offline these days too. In addition, decision tree models are more interpretable as they simulate the human decision-making process. Decision trees, being a non-linear model, can handle both numerical and categorical features. number of white spaces at any level = (max number of element in tree)//2^level. plot_tree(model, num_trees=4, ax=ax) plt. print the value and white spaces find my Riple. Decision trees are a supervised machine learning model used for both classification and regression tasks (CART). The tutorial covers: Preparing the data. I am trying to evaluate a relevance of features and I am using DecisionTreeRegressor() The related part of the code is presented below: # TODO: Set a random state. Knowing this, the steps that we need to follow in order to code a decision tree from scratch in Python are simple: Calculate the Information Gain for all variables. This can also be done by calculating Entropy instead of Gini Impurity. Read more in the User Guide. The train set will be used to train the model, while the test set will be used to evaluate the effectiveness of the model. 1- (p²+q²) where p =P (Success) & q=P (Failure) Calculate Gini for Apr 17, 2022 · April 17, 2022. 800000011920929 else to node 2. Jan 22, 2022 · Jan 22, 2022. predict (X_test) 5. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical Click here to buy the book for 70% off now. data from sklearn. In this article, We are going to implement a Decision tree in Python algorithm on the Balance Scale Weight & Distance Nov 19, 2023 · Nov 18, 2023. Setting Up Your Python Environment. Jan 12, 2022 · Decision Tree Python - Easy Tutorial. csv") print(df) Run example ». Since the decision tree follows a supervised approach, the algorithm is fed with a collection of pre-processed data. trees_to_dataframe(). Visualizing decision trees is a tremendous aid when learning how these models work and when Jun 3, 2020 · Classification-tree. I had the same problem recently and the only way I found is by trying diffent figure size (it can still be bluery with big figure. How to make the tree stop growing when the lowest value in a node is under 5. Steps to Calculate Gini impurity for a split. DataFrame(columns = ["Real", "Predicción"]) #CREATE AN EMPTY PANDAS DATAFRAME. Please don't convert strings to numbers and use in decision trees. Reading the processed dataset. clf = DecisionTreeClassifier (max_depth=3) #max_depth is maximum number of levels in the tree. A Decision Tree is formed by nodes: root node, internal nodes and leaf nodes. The options are “gini” and “entropy”. Feb 3, 2019 · I am training a decision tree with sklearn. Decision trees are an intuitive supervised machine learning algorithm that allows you to classify data with high degrees of accuracy. R-> 200. The function to measure the quality of a split. plot_tree(dt,fontsize=10) Im looking to replace these X [featureNumber] with the actual feature name. read_csv ("data. from igraph import *. fit (breast_cancer. clf = DecisionTreeClassifier () # Train Decision Tree Classifier. One cannot trace how the algorithm works unlike decision trees. When I run the print function, it returns the given score: -0. This way you can reconstruct the tree, since for each row of the dataframe, the node ID has directed edges to Yes and No. Decision trees, non-parametric supervised learning algorithms, are explored from basics to in-depth coding practices. Greater values of ccp_alpha increase the number of nodes pruned. Update Mar/2018: Added alternate link to download the dataset as the original appears […] Mar 13, 2021 · Plotly can plot tree diagrams using igraph. Pandas has a map() method that takes a dictionary with information on how to convert the values. Apr 17, 2022 · In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. Decision region: region in the feature space where all instances are assigned to one class label information_gain(data[ 'obese' ], data[ 'Gender'] == 'Male') 0. tree import _tree. Target01) df['target'] = label_encoder. Returns: self. Each tree consists of a root node from which we can access the elements of the tree. subplots(figsize=(30, 30)) xgb. The random forest is a machine learning classification algorithm that consists of numerous decision trees. They are easy to implement, explain and are among the Apr 30, 2023 · Decision Trees are widely used for solving classification problems due to their simplicity, interpretability, and ease of use. Decision Tree for Classification. You can use sklearn's LabelEncoder to transform your strings to integers. You can find the score function implementatioin and some explanation below here and below: Dec 5, 2011 · How do I suppress the None's that are printed when I run the function? TREE_PRINT(sampletree) split: foo {'cut': 150} L-> 100. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. If you want to know the actual parameters of Dec 13, 2020 · Modelling the nodes of the tree. fit(x,y) Apr 21, 2017 · graphviz web portal. Max_depth: defines the maximum depth of the tree. The Yes column contains the ID of the yes-branch, and the No column of the no-branch. predict_proba(). iloc[:,2]. Decision Tree Code : See full list on pythoninoffice. When we use a decision tree to predict a number, it’s called a regression tree. Training the model. The depth of a tree is the maximum distance between the root and any leaf. While creating a decision tree, the key thing is to select the best attribute from the total features list of the dataset for the root node and for sub-nodes. pdf") A 1D regression with decision tree. feature for left & right children. Our primary packages involved in building our model are pandas, scikit-learn, and NumPy. show() To save it, you can do. There is no way to handle categorical data in scikit-learn. Aug 12, 2014 · 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. target) Nov 22, 2021 · Classification and Regression Trees (CART) can be translated into a graph or set of rules for predictive classification. To exemplify the implementation of a classification tree, we will use a dataset with a few instances that has been previously treated with a full EDA. plt. Feature importances are provided by the fitted attribute feature_importances_ and they are computed as the mean and standard deviation of accumulation of the impurity decrease within each tree. Plot a decision tree. label_encoder = preprocessing. def tree_to_code(tree, feature_names): tree_ = tree. fit(df. For example, my input might look like this: . 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. Standardization) Decision Regions. Decision Tree From Scratch in Python. maximum number of elements of h height tree = 2^h -1; considering root level height as 1. The maximum depth of the tree. The algorithm creates a set of rules at various decision levels such that a certain metric is optimized. Step 1. values #Creating a model object and fiting the data reg = DecisionTreeRegressor(random_state=0) reg. Apr 14, 2021 · A decision tree algorithm (DT for short) is a machine learning algorithm that is used in classifying an observation given a set of input features. Starting from the root node, each node contains zero or more nodes connected to it as children. We need to write it. Let’s start with the former. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. plotly as py. In the following examples we'll solve both classification as well as regression problems using the decision tree. import graphviz. Decision Tree Classifier is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. #Set Up Tree with igraph. Aug 22, 2018 · First is that the order of arguments in wrong. In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. Python Decision-tree algorithm falls under the category of supervised learning algorithms. Here is the code to produce the decision tree. After importing Mar 28, 2024 · Building Your First Decision Trees in Python. In defining each node of the tree (pydot graph), I appoint it a unique (and verbose) name and a brief label. 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. copy(), config) #CREATE THE DECISION TREE BASED OF THE CONFIGURATION ABOVE. 649574327334. Warning. Share Jun 9, 2023 · A Python tree is a data structure in which data items are connected using references in a hierarchical manner in the form of edges and nodes. 1. The selection of best attributes is being achieved with the help of a technique known as the Attribute Selection Measure (ASM). This makes it very easily to create new instances of certain models (although you could also use sklearn. Impurity-based feature importances can be misleading for high cardinality features (many unique values). Note: Both the classification and regression tasks were executed in a Jupyter iPython Notebook. DecisionTreeClassifier() the max_depth parameter defaults to None. fit (X_train,y_train) #Predict the response for test dataset. transform(df. Each decision tree in the random forest contains a random sampling of features from the data set. We will also be discussing three differe Return the depth of the decision 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. A node may have zero children (a terminal node), one child (one side makes a prediction directly) or two child nodes. dot', 'w') as my_file: my_file = tree. Criterion: defines what function will be used to measure the quality of a split. So you can do this one of following of two ways, 1) Change line where you collect dot_data value in graph to. Once you have built your decision tree clf, simply: from sklearn. base. # I do not endorse importing * like this. 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. The code below is based on StackOverflow answer - updated to Python 3. They help when logistic regression models cannot provide sufficient decision boundaries to predict the label. In this Aug 31, 2017 · type(graph) <type 'list'>. I'm building off of the 'printtree' function from page 151 in Programming Collective Intelligence. com Aug 2, 2019 · The scikit-learn documentation has an example here on how to get out the information from trees. target) tree. Predicting and accuracy check. Jun 8, 2023 · In this blog post, we’ll walk through a step-by-step guide on how to implement decision trees in Python using the scikit-learn library. 3. Calculate Gini impurity for sub-nodes, using the formula subtracting the sum of the square of probability for success and failure from one. png. We are only interested in first element of the list. On SciKit - Decission Tree we can see the only way to do so is by min_impurity_decrease but I am not sure how it specifically works. 10. children_left/right gives the index to the clf. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for your model, how Feb 25, 2021 · Extract Code Rules. export_graphviz method (graphviz needed) plot with dtreeviz package (dtreeviz and graphviz needed) 1. Oct 29, 2019 · model = chef. tree import DecisionTreeClassifier. by using the BFS get the lists of list contains elements of each level. from sklearn. datasets import load_iris iris = load_iris() import numpy as np ytrain = iris. Decision Trees is a type of supervised learning algorithms in machine learning, used for both classification and regression tasks. In particular, children_right and children_left properties seem to be useful. 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. g. Returns: routing MetadataRequest Feb 1, 2022 · One more thing. # SAVE ALL REAL VS ESTIMATED VALUES IN THE ABOVE DATAFRAME. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a import pandas. I need to obtain the MSE of each leaf node, and carry out subsequent operations according to the MSE. The treatment of categorical data becomes crucial during the tree Apr 1, 2020 · As of scikit-learn version 21. A decision tree classifier. 4. This data is used to train the algorithm. 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. A decision tree consists of the root nodes, children nodes 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. It works for both continuous as well as categorical output variables. target xtrain = iris. 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. clf = clf. When I use: dt_clf = tree. clf. To extract the decision rules from the decision tree we use the sci-kit-learn library. Jun 30, 2020 · I m building a multiclass decision tree classifier and trying to print decision tree output in textual form. It splits data into branches like these till it achieves a threshold value. Separate the independent and dependent variables using the slicing method. DecisionTreeClassifier(criterion = "entropy") dtree = dtree. PySpark’s MLlib library provides an array of tools and algorithms that make it easier to build, train, and evaluate machine learning models on distributed data. One option is to use the decision tree classifier in Spark - in which you can explicitly declare the categorical features and their ordinality. 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. so instead of it displaying X [0], I would want it to Sep 25, 2020 · You can also use the get_params method define for (I believe) all scikit-learn models, as they inherit from sklearn. May 9, 2017 · According to the documentation, there's tree_ attribute, you can traverse that tree to find any properties of interest. Let’s use a relevant example: the Iris dataset, a Oct 3, 2020 · In this tutorial, we'll briefly learn how to fit and predict regression data by using the DecisionTreeRegressor class in Python. The decision tree is like a tree with nodes. Nov 7, 2023 · First, we’ll import the libraries required to build a decision tree in Python. We provide the y values because our model uses a supervised machine learning algorithm. externals. decision_path(xtrain) The output is this: Aug 27, 2020 · Plotting individual decision trees can provide insight into the gradient boosting process for a given dataset. – Downloading the dataset Mar 27, 2021 · Loading csv data in python, (using pandas library) Training and building Decision tree using ID3 algorithm from scratch; If we print the tree we can see like that: {'Outlook': A python library for decision tree visualization and model interpretation. This algorithm is also called CART (Classification and Regression Trees). It serves as the foundation for more sophisticated models like Random Forest, Gradient Boosting, and XGBoost. max_depth, store away the response and take an average after completing our iteration: Try iterate over the trees in the forest and print them out one by one: from sklearn import tree i_tree = 0 for tree_in_forest in forest. values y =df. 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. predict(iris. Aug 18, 2018 · Conclusions. png, I see the verbosenode names and not the node labels. import igraph. datasets import load_iris. Aug 20, 2021 · Creating and visualizing decision trees with Python. tree import DecisionTreeClassifier dtree = DecisionTreeClassifier() fitted_tree = dtree. None. I came across this solution on the net: Dec 24, 2023 · The Decision Tree stands as one of the most famous and fundamental Machine Learning Algorithms. plot_tree without relying on the dot library which is a hard-to-install dependency which we will cover later on in the blog post. The topmost node in a decision tree is known as the root node. In decision tree classifier, the Jun 20, 2019 · sklearn's decision tree needs numerical target values. I'm just having trouble figuring out how to print a recursion tree. from sklearn import preprocessing. decision tree visualization with graphviz. X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) Next, we create and train an instance of the DecisionTreeClassifer class. weighted_n_node_samples to get the gini/entropy value and number of samples at the each node & at it's children. decision_path(X_test) # Similarly, we can also have the leaves ids reached by each sample. You need to use the predict method. A tree can be seen as a piecewise constant approximation. I was looking for a possible implementation of tree printing, which prints the tree in a user-friendly way, and not as an instance of object. If it DTR will sort of create a partition level for all the values Check the graph - Click here from sklearn. The branches depend on a number of factors. Mar 23, 2018 · Below is a snippet of the decision tree as it is pretty huge. # indicator matrix at the position (i, j) indicates that the sample i goes. We can create a Python class that will contain all the information of all the nodes of the Decision Tree. export_text method; plot with sklearn. 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. plot_tree method (matplotlib needed) plot with sklearn. 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. Cost complexity pruning provides another option to control the size of a tree. dt = DecisionTreeClassifier() dt. We have to convert the non numerical columns 'Nationality' and 'Go' into numerical values. A Decision Tree algorithm is a supervised learning algorithm for classification and regression tasks. when my dependent variable is 1 this sklearn's export_text lib is giving correct results but it's not the case when we increase no of variables. Once the graphviz web portal opened. To make a decision tree, all data has to be numerical. Oct 19, 2016 · export_graphviz(treeclf, out_file='tree_titanic. As a result, it learns local linear regressions approximating the sine curve. How can I run this command in jupyter to visualize the tree? Thanks. Sep 10, 2015 · 17. fit(X,y) # Visualising the Decision Tree Regression results (higher resolution) X_grid = np Oct 8, 2021 · Performing The decision tree analysis using scikit learn. We'll apply the model for a randomly generated regression data and Boston housing dataset to check the performance. Decision-tree algorithm falls under the category of supervised learning algorithms. 0005506911187600494. graph_objs as go. May 14, 2024 · Decision Tree is one of the most powerful and popular algorithms. Throughout this article, I’ll walk you through training a Decision Tree in Python using scikit-learn on the Iris Species Dataset, known as Nov 16, 2023 · In this section, we will implement the decision tree algorithm using Python's Scikit-Learn library. plot_tree(decision_tree, *, max_depth=None, feature_names=None, class_names=None, label='all', filled=False, impurity=True, node_ids=False, proportion=False, rounded=False, precision=3, ax=None, fontsize=None) [source] #. I am using the above code, but when I try to run the (dot command) in the terminal, it doesn't work. Another disadvantage is that they are complex and computationally expensive. get_metadata_routing [source] # Get metadata routing of this object. impurity & clf. Choose the split that generates the highest Information Gain as a split. fit(X_train, y_train) # plot tree. Dec 30, 2022 · The splitting criteria are chosen by an algorithm, such that the Gini index always remains minimum for each split. Learn more about this here. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. For exemple, to plot the 4th tree, use: fig, ax = plt. ensemble import RandomForestClassifier clf = RandomForestClassifer(n_estimators = 10) clf = clf. In this article, we’ll create both types of trees. dot -o tree_titanic. You will learn how to build a decision tree, how to prune a decision tree May 27, 2018 · The decision_path. fit(X=xtrain,y=ytrain) predictiontree = dtree. Please check User Guide on how the routing mechanism works. A Decision Tree is a supervised Machine learning algorithm. # through the node j. dot', feature_names=feature_cols) At the command line, run this to convert to PNG: dot -Tpng tree_titanic. Sequence of if-else questions about individual features. 2. figure(figsize=(20,16))# set plot size (denoted in inches) tree. tree_ also stores the entire binary tree structure, represented as a Dec 17, 2019 · In the generated decision tree regression model, there is an MSE attribute when using graphviz to view the tree structure. iloc[:,1:2]. X = df # data without target class. It can be used to predict the outcome of a given situation based on certain input parameters. This tree seems pretty long. In classification_report(), actual labels (true ground truths) are first and predicted labels are second. 0 (roughly May 2019), Decision Trees can now be plotted with matplotlib using scikit-learn’s tree. My problem is that in the resulting figure that I get by writing to a . Parameters: criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. # Export resulting tree to DOT source code string. It learns to partition on the basis of the attribute value. Sep 10, 2017 · 5. New nodes added to an existing node are called child nodes. There isn't any built-in method for extracting the if-else code rules from the Scikit-Learn tree. dot_data = export_graphviz(clf, Feb 5, 2020 · Decision Tree. For the modeled fruit classifier, we will get the below decision tree visualization. Let’s get started. Building a Decision Tree in Python demystifies the process of data analysis and machine learning, making it accessible even to beginners. 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. Objective: infer class labels; Able to caputre non-linear relationships between features and labels; Don't require feature scaling(e. 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 Mar 21, 2019 · Now we can iterate over its estimators_ attribute containing each decision tree. tree import export_graphviz. fit(iris. For each decision tree, we inquiry the attribute tree_. In this tutorial you will discover how you can plot individual decision trees from a trained gradient boosting model using XGBoost in Python. After training the tree, you feed the X values to predict their output. The decision trees is used to fit a sine curve with addition noisy observation. Load the data set using the read_csv () function in pandas. See Permutation feature importance as Mar 9, 2021 · from sklearn. Python. When our goal is to group things into categories (=classify them), our decision tree is a classification tree. ok cy wu lf ue va it us hd tt