Sklearn plot tree too small. This is useful in order to create lighter ROC curves.

The code below plots a decision tree using scikit-learn. First the "training data", which should be a 2D array, and second the "target values". So you can do this one of following of two ways, 1) Change line where you collect dot_data value in graph to. figure 的 figsize 或 dpi 参数来控制渲染的大小。. so instead of it displaying X [0], I would want it to Clustering — scikit-learn 1. float32 and np. export_graphviz(model, feature_names=feature_names, class_names=class_names, filled=True, rounded=True, special_characters=True, out_file=None, ) graph = graphviz. But value? machine-learning. 012, which would suggest that none of the features are important. . 最近気づい Sep 19, 2016 · svr = SVR(kernel=kernel, C=c, degree=4) svr. Pre Dec 3, 2016 · 2. dt = DecisionTreeClassifier() dt. After plotting a sklearn decision tree I check what it says in each box and there is one feature "value" that I am not sure what it refers. plot_tree(classifier); Jun 5, 2021 · According to the documentation of plot_tree for its filled parameter:. First load the copy of the Iris dataset shipped with . 2, random_state=55) # Use the random grid to search for best hyperparameters. Fitted classifier or a fitted Pipeline in which the last estimator is a classifier. 7. datasets import load_iris from sklearn import tree iris = load_iris() clf = tree. set_figheight(15) fig. The i-th element of each # array holds information about the node `i`. Nov 20, 2023 · Pruning is a process of removing or collapsing some nodes or branches of a decision tree, to reduce its size and complexity. dtype{np. This is documentation for an old release of Scikit-learn (version 0. To convert this to the absolute values, you can multiply these by the corresponding value of DecisionTreeClassifier. Jun 1, 2022 · if you use xgboost, there is already a plot_tree function. A single estimator thus handles several joint classification tasks. The only difficulty was to convert sklearn's children_ output to the Newick Tree format that can be read and understood by ete3. It's kind of like a pixel-grid; I shrunk the size down to an array of only 2000 and noticed that the coordinates were just BaggingClassifier. The classes in the sklearn. fit(X, y) dot_data = tree. Script File: Loads, normalises, and organises the Iris dataset from Sklearn package. feature_names, class_names=iris. The way I managed to plot the damn dendogram was using the software package ete3. float32 and if a sparse matrix is provided to a sparse csr_matrix. tree_. I am building a decision tree in scikit-learn then want to produce a pdf of the tree. set_style() the plot output from tree. could help but if it isn't you have to upgrade the whole python version. Dec 6, 2019 · Plot tree is available after sklearn version > 0. 9”. Changed in version 0. set_figwidth(8) fig. For exemple, to plot the 4th tree, use: fig, ax = plt. plot_tree) will not show anything if you don't have plt. fit_transform(data) vec. hierarchy import dendrogram from sklearn. 22: The default value of n_estimators changed from 10 to 100 in 0. The example compares prediction result of linear regression (linear model) and decision tree (tree based model) with and without discretization of real-valued features. tight_layout() This provides really good layout with customisable height and width. __version__) If the version shows less than 0. Mar 20, 2021 · Just increase figsize=(50,30), adjust dpi=300 and apply the code to save the image in png. set_style('whitegrid') #Note: this can be any option for set_style Oct 31, 2016 · One thing may needs to be changed is from fig. Parameters: criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. show() somewhere. The input samples. 117 2 13. A simpler way is to let sklearn to do most of May 15, 2023 · Sklearn plot_tree plot is too small. I work on OS X and the graphviz stuff seems to be no longer properly supported there. predict(iris. sklearn_evaluation. 22 Plot classification probability K-means Clustering Plot H Mar 15, 2020 · Because plot_tree is defined after sklearn version 0. export_graphviz method (graphviz needed) plot with dtreeviz package (dtreeviz and graphviz needed) Jun 8, 2019 · make use of feature_names and class_names parameters:. plt. scikit-learn. figure(figsize=(20, 20)) before plotting, but the figure size did not change with output text 'Figure size 1440x1440 with 0 Axes'. 0. See Permutation feature importance as Jul 18, 2018 · 1. 要绘制的决策树。. It does not produce the nodes or arrows to actually visualize the tree. tree' has no attribute 'plot_tree' Although I install 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. Read more in the User Guide. pylab import rcParams. 24). The number of splittings required to isolate a sample is lower for outliers and higher for plot_tree. The decision tree is basically like this (in pdf) is_even<=0. Supervised learning. allow_single_clusterbool, default=False. Read more about the export The decision tree estimator to be exported. criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. As is shown in the result before discretization, linear model is fast to build and relatively straightforward to May 20, 2023 · The size of boxes representing tree nodes in decision tree plot in Scikit-learn can greatly affect the readability and interpretation of the model. clf = tree. Cássia Sampaio. In the past, it would take me about 10 to 15 minutes to write a code with two different packages that can be done with two lines of code. May 20, 2016 · from xgboost import plot_tree. png") 3. Google Colabプリインストールされているパッケージはそのまま使っています。. answered Apr 14, 2020 at 1:38. DecisionTreeClassifier() Cost complexity pruning provides another option to control the size of a tree. The tree_. tree. Clustering of unlabeled data can be performed with the module sklearn. figure to control the size of the rendering. cluster import AgglomerativeClustering from sklearn. 7 python and solve it by installing 3. 22. The hint to look at is the return value of the method (which is "fig" and "ax"). plot_tree(decision_tree=clf, feature_names=feature_names, class_names=class_names, filled=True, rounded=True, fontsize=10, max_depth=4,dpi=300) #adjust the dpi to the parameter that fits best your output 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. Scikit-learn defines a simple API for creating visualizations for machine learning. 02. Only np. Clustering #. class_namesarray-like of shape (n_classes PCA. plot_tree(clf) # the clf is your decision tree model The example output is similar to what you will get with export_graphviz: You can also try dtreeviz package. tree_ also stores the entire binary tree structure, represented as a Aug 31, 2017 · type(graph) <type 'list'>. figsize'] = 80,50. savefig("temp. sometree = . Oct 20, 2016 · After you fit a random forest model in scikit-learn, you can visualize individual decision trees from a random forest. #. They are however often too small to be representative of real world machine learning tasks. Mar 9, 2021 · from sklearn. mean() The problem is it is taking too long to run, even for the baseline model. PV8 PV8. figure の figsize または dpi 引数を使用して、レンダリングのサイズを制御します The permutation importance on the right plot shows that permuting a feature drops the accuracy by at most 0. plot_tree(model) Bottom line: there will probably be more broken things in that material. May 12, 2017 · This is due to the fact the step size/plot step is very small . Let's suppose the label I want to add is "lambda", and I already have a different value for each terminal node based on their node ID. I am definitely looking forward to future updates that support random forest and ensemble models. 可视化会自动适应轴的大小。. DecisionTreeClassifier(random_state=0). clf = DecisionTreeClassifier(random_state=0) iris = load_iris() tree = clf. 環境. The function to measure the quality of a split. The nodes have the following structure: But I don't understand what does the value = [2417, 1059] mean. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical plot_tree. It will give you much more information. feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their performance on very high-dimensional datasets. 5) or development (unstable) versions. I have added plt. Apr 10, 2019 · ~\Anaconda3\lib\site-packages\sklearn\tree\tree. Plot specified tree. data) Aug 24, 2023 · I want to add a custom label above "value" in a decision tree made with the DecisionTreeRegressor class from sklearn. from sklearn import tree. 6-4)] on linux Package used (python/R/jvm/ The standard approach for HDBSCAN* is to use an Excess of Mass ( "eom" ) algorithm to find the most persistent clusters. get_feature_names() #Shows feature names. Source(dot_data) graph plot_confusion_matrix is deprecated in 1. Share. MosQuan. tree. 0, iterated_power='auto', n_oversamples=10, power_iteration_normalizer='auto', random_state=None) [source] #. So it is essentially taking baby steps across the domain of the data's min and max and plotting/filling as it goes, according to the model's predictions. 绘制决策树。. fit(train_features, train_target) score = svr. export_text method; plot with sklearn. I had the same issue on 3. A Bagging classifier. Jun 20, 2022 · This new-ish function is much easier to use than the older Graphviz visualization. float64 are supported. save () to fig. Similarly, the change in accuracy score computed on the test set Aug 19, 2020 · Rでは決定木の可視化は非常に楽だが、Pythonでは他のツールを入れながらでないと、、、と昔は大変だったのですが、現在ではsklearnのplot_treeだけで簡単に表示できるようになっています。. Leaf nodes have labels like leaf 2: 0. 5. plot_tree too small，大家都在找解答。sklearn. Here is the code. This way, you can generate 6 models and see which parameters lead to the best score, which will be the best model to choose, given these parameters. fit(iris. If None, then nodes are expanded until all Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. There should be an option to specify image size or resolution. Parameters: estimatorestimator instance. Maybe you set a maximum depth of 2, or some other parameter that prevents additional splitting. 24. getvalue()) 2) Or collect entire list in graph but just use first element to be sent to pdf. 1 documentation. figure(figsize=(50,30)) artists = sklearn. 24 Release Highlights for scikit-learn 0. For checking Version Open any python idle Running below program. IsolationForest example. pdf") May 15, 2020 · Am using the following code to extract rules. float32, np. Pixel importances with a parallel forest of trees; Plot class probabilities calculated by the VotingClassifier; Plot individual and voting regression predictions; Plot the decision boundaries of a VotingClassifier; Plot the decision surfaces of ensembles of trees on the iris dataset; Prediction Intervals for Gradient Boosting Regression Apr 19, 2023 · Plot Decision Boundaries Using Python and Scikit-Learn. plot_tree(sometree) plt. model_gini_class = tree. fig = plt. class sklearn. DecisionTreeClassifier(criterion='gini') User Guide. plot_tree(clf); Jul 15, 2018 · original_tree. An example using IsolationForest for anomaly detection. Increasing false positive rates such that element i is the false positive rate of predictions with score >= thresholds[i]. fit(X_train, y_train) # plot tree. pyplot as plt. 4. target) tree. – David Visualizations — scikit-learn 1. 決定木をプロットします。. You need to use the predict method. Both the number of properties and the number of classes per property is greater than 2. The label1 is marked "o" and not "e". 3. n_node_samples for the same node index. 5 /\ / \ label1 label2 The problem is this. While the functional API allows you to quickly generate out-of-the-box plots and is the easiest to get started with, the OOP API offers more flexibility to compare models using a simple synatx, i. model_selection import cross_val_score. The number of trees in the forest. savefig("decistion_tree. answered May 4, 2022 at 8:27. You pass the fit model into the plot_tree() method as the main argument. Warning. _tree import DepthFirstTreeBuilder Gallery examples: Release Highlights for scikit-learn 1. tree import DecisionTreeClassifier from sklearn import tree model = DecisionTreeClassifier() model. plot_tree(clf,filled=True,rounded=True) plt. 2. Non-leaf nodes have labels like Column_10 <= 875. Linear dimensionality reduction using Singular Value Decomposition of the data to Aug 13, 2018 · grid_resolution=5) fig. # First create the base model to tune. 请阅读 User Guide 了解更多信息。. Update Mar/2018: Added alternate link to download the dataset as the original appears […] This example plots the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy. To plot or save the tree first we need to export it to DOT format with export_graphviz method. The effect is depicted by checking the statistical performance of the model in terms of training score and testing score. # plot decision tree from xgboost import XGBClassifier from xgboost import plot_tree import matplotlib. 1. 21 then you need to upgrade the sklearn library. Plot a decision tree. My current tree is: I'm plotting it with: sklearn. Sep 12, 2015 · 4. 422, which means “this node is a leaf node, and the predicted These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in scikit-learn. Steps/Code to Reproduce. data, iris. plot_confusion_matrix package, but the default figure size is a little bit small. tree import plot_tree. The decision tree correctly identifies even and odd numbers and the predictions are working properly. In the case considered here, we simply what to make a fit, so we do not care about the notions too much, but we need to bring the first input to that function into the lightgbm. savefig () saving the tree results in an image of unreadably low resolution. decision-trees. py in 38 from . Here, we compute the learning curve of a naive Bayes classifier and a SVM classifier with a RBF kernel using the digits dataset. score(test_features, test_target) print kernel, c, score. In this article, we explore different methods to optimize the size of these boxes, including adjusting the figure size, changing the font size, and using custom node sizes. For example, there are 29 nodes. 2. The example: You can find a comparison of different The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros. plot #. We will also pass the features and classes names, and customize the plot so that each tree node is displayed May 22, 2020 · For those coming in with more recent versions of sklearn (mine is 1. fit takes two arguments. An array containing the feature names. Oct 27, 2021 · from sklearn. However if I put class_names in export function as . png: Note also that pydotplus. pip install --upgrade sklearn. The visualization is fit automatically to the size of the axis. class_names=['e','o'] 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 sklearn. Raywho. See decision tree for more information on the estimator. from sklearn. _criterion import Criterion 41 from . Added in version 0. Alternatively you can instead select the clusters at the leaves of the tree – this provides the most fine grained and homogeneous clusters. savefig('foo. feature_namesarray-like of shape (n_features,), default=None. _splitter import Splitter 42 from . compute_node_depths() method computes the depth of each node in the tree. datasets import load_breast_cancer. 7. plot_tree(model, num_trees=4, ax=ax) plt. After training the tree, you feed the X values to predict their output. 訓練、枝刈り、評価、決定木描画をしていきます。. Visualizations #. Source(pydot_graph. vec = DictVectorizer() data_vectorized = vec. 17: parameter drop_intermediate. 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. Improve this answer. validation import check_is_fitted 39 ---> 40 from . How can I fix this? Mar 18, 2015 · I came across the exact same problem some time ago. Removing features with low variance 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. sklearn. So why is it taking such a long time to run even for something as simple sklearn. filled: bool, default=False When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output. 表示されるサンプル数は、存在する可能性のあるsample_weightsで重み付けされます。. subplots(figsize=(30, 30)) xgb. Using KBinsDiscretizer to discretize continuous features. rcParams['figure. But there is an errror appeared in the console. The example decision tree will look like: Then if you have matplotlib installed, you can plot with sklearn. # create tree object. Pruning can be done either before or after the tree is fully grown. tree import DecisionTreeClassifier from sklearn import tree classifier = DecisionTreeClassifier(max_depth = 3,random_state = 0) tree. so no need to use sklearn. 6 20120305 (Red Hat 4. 6. 21 版本中的新增内容。. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both Last remark: don't get deceived by the superficial differences in the tree layouts, which reflect only design choices of the respective visualization packages; the regression tree you have plotted (which, admittedly, does not look much like a tree) is structurally similar to the classification one taken from the docs - simply imagine a top-down Oct 6, 2021 · Regression tree. Source object in your question: import graphviz gvz_graph = graphviz. 9, which means “this node splits on the feature named “Column_10”, with threshold 875. 使用 plt. 2 Release Highlights for scikit-learn 0. Try the latest stable release (version 1. plot_tree: tree. LinearRegression. plot_tree (decision_tree, *, max_depth=None, feature_names=None, class_names=None, label=&#39;all&#39 Dec 3, 2016 · 2. Greater values of ccp_alpha increase the number of nodes pruned. Maybe the decision tree used a fraction of the features as a regularization technique. Follow answered May 15, 2023 at 12:46. This saved image should look better. decomposition. AttributeError: module 'sklearn. model_selection import train_test_split. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. value gives an array of the relative size of the classes. Iris plants dataset# Data Set Characteristics: Number of Instances: 150 (50 in each of three classes) Number of Attributes: May 31, 2020 · I want to plot the tree corresponding to best fit parameter that gridsearch has found out. Sep 10, 2015 · 17. 13で1Google Colaboratory上で動かしています。. As you can see, visualizing a decision tree has become a lot simpler with sklearn models. tree import DecisionTreeClassifier. In this tutorial you will discover how you can plot individual decision trees from a trained gradient boosting model using XGBoost in Python. metrics. plot_tree(finalmodel, num_trees=X) hope this will help, I think you should set up the matplotlib parameters first. Gallery examples: Release Highlights for scikit-learn 1. PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0. plot_roc_curve — scikit-learn 0. Learning curves show the effect of adding more samples during the training process. 5. My workflow to output the tree is roughly as follows. The sample counts that are shown are weighted with any sample_weights that might be present. The Isolation Forest is an ensemble of “Isolation Trees” that “isolate” observations by recursive random partitioning, which can be represented by a tree structure. We also provide code examples and best practices for creating clear and Dec 5, 2019 · A lot of the information coming from these arrays can be seen on the tree plot. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0. 2 documentation. scikit-learnのDecisionTreeClassifierの基本的使い方を解説します。. Repository consists of a script file, hyperplane generator function and the gif file. Python3. The Plot API supports both functional and object-oriented (OOP) interfaces. graph_from_dot_data(dot_data. Each node in the graph represents a node in the tree. This is in contradiction with the high test accuracy computed as baseline: some feature must be important. If None, output dtype is consistent with input dtype. This is the first time I am facing this problem as usually any kind of tree based model does not take this long. subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300) tree. Graph objects have a to_string() method which returns the DOT source code string of the tree, which can also be used with the graphviz. (graph, ) = pydot. さらにplot_treeはmatplotlibと同様に操作できるため、pandasなどに慣れて Maybe the data was perfectly separated using that variable. max_depthint, default=None The maximum depth of the tree. This is useful in order to create lighter ROC curves. cluster. Getting the data into shape. 1. So unless you really need the DOT file for some reasons, you should be able to do this: from sklearn. fig, axes = plt. Please help me plot a tree of higher resolution as the image gets blurred when I increase the tree depth. 視覚化は軸のサイズに自動的に適合します。. Visualize the Decision Tree with Graphviz. figure(figsize=(20,16))# set plot size (denoted in inches) tree. pyplot as plt # fit model no training data model = XGBClassifier() model. plot_tree. The code below first fits a random forest model. 0 (roughly May 2019), Decision Trees can now be plotted with matplotlib using scikit-learn’s tree. Principal component analysis (PCA). The from Jul 30, 2022 · Save the Tree Representation of the plot_tree method… fig. The first line will be the column and the value where it splits, the gini the "disorder" of the data and sample the number of samples in the node. . Use the figsize or dpi arguments of plt. Open Anaconda prompt and write below command. from matplotlib. plot_tree() only produces the labels of each split. Apr 20, 2020 · I tried to plot confusion matrix with Jupyter notebook using sklearn. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. import seaborn as sns sns. Multiclass-multioutput classification (also known as multitask classification) is a classification task which labels each sample with a set of non-binary properties. plot_tree: Dec 4, 2019 · Below are my code: from sklearn import tree. A decision tree classifier. I find this incredibly useful for interpretation especially of the nodes on a tree plot are very small and hard to see. target_names) I want to show decision tree figure for my data visualization. If None generic names will be used (“feature_0”, “feature_1”, …). It can be an instance of DecisionTreeClassifier or DecisionTreeRegressor. import sklearn print (sklearn. png') However, the saved image is totally blank. Operating System: linux Compiler: GCC 4. Internally, it will be converted to dtype=np. In the example shown, 5 of the 8 leaves have a very small amount of samples (<=3) compared to the others 3 leaves (>50), a possible sign of over-fitting. plot_tree method (matplotlib needed) plot with sklearn. Impurity-based feature importances can be misleading for high cardinality features (many unique values). import matplotlib. datasets import load_iris Jul 23, 2021 · base_dtr_score = cross_dtr_score. plot_tree(dt,fontsize=10) Im looking to replace these X [featureNumber] with the actual feature name. Jan 14, 2021 · I plotted my sklearn decision tree using the plot_tree function. In jupyter notebook the following plots the decision tree: from sklearn. Fit the gradient boosting model. graphviz also helps to create appealing tree visualizations for the Decision Trees. float64}, default=None. One can count them on the tree plot as well. show() To save it, you can do. to_string()) gvz_graph 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. 表示 Jan 31, 2021 · The font is too small to be visualized so I wish to save the image and view it locally instead of on Jupyter. Aug 27, 2020 · Plotting individual decision trees can provide insight into the gradient boosting process for a given dataset. Let’s get started. This package is able to flexibly plot trees with various options. 0 and will be removed in 1. 13. Tree-based models have become a popular choice for Machine Learning, not only due to their results, and the need for fewer transformations when working with data (due to robustness to input and scale invariance), but also because there is a way to take a peek inside of sklearn. ensemble import RandomForestClassifier. graphviz. 21. The desired data-type for the output. We also show the tree structure of a model built on all of the features. datasets import load_iris. We provide Display classes that expose two methods for creating plots: from Aug 12, 2014 · Then if you have matplotlib installed, you can plot with sklearn. Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that, given train data, returns an array of integer 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. import numpy as np from matplotlib import pyplot as plt from scipy. show() # mandatory on Windows. 6,172 7 7 gold badges 49 49 silver badges 100 100 Plot a decision tree. So I wrote a simple ASCII based decision tree visualizer for the sklearn DecisionTreeClassifier: tree _print (see attached). answered Mar 14, 2017 at 12:36. import pandas as pd. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. fit(X, y) # plot single tree plot_tree(model) plt. plot_tree(clf, feature_names=iris. We are only interested in first element of the list. pip install --upgrade scikit-learn Apr 1, 2020 · As of scikit-learn version 21. 1 ), instead of absolute values, clf. The SVM-Decision-Boundary-Animator GitHub repo animates the SVM Decision Boundary Hyperplane on the Iris data using matplotlib. Use one of the following class methods: from_predictions or from_estimator. utils. In the case considered here, we simply what to make a fit, so we do not care about the notions too much, but we need to bring the first input to that function into the If using scikit-learn and seaborn together, when using sns. tree import plot_tree plot_tree(t) (where t is an instance of DecisionTreeClassifier) This is the output: Sklearn plot_tree plot is too small. 3 Recognizing hand-written digits A demo of K-Means clustering on the handwritten digits data Feature agglomeration Various Agglomerative Clu Jul 12, 2018 · SVM-Decision-Boundary-Animator. ##set up the parameters. 显示的样本计数使用可能存在的任何样本权重进行加权。. DecisionTreeClassifier(max_leaf_nodes=8) specifies (max) 8 leaves, so unless the tree builder has another reason to stop it will hit the max. e, plot1 + plot2; or to customize the style and elements in the plot. png: resized_tree. Also the train and test dataset is not huge. Feature selection #. metrics import accuracy_score. Using these two return values, extra options become available such as setting the width and height, independently. At least on windows matplotlib (which is used to show the tree with tree. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. edited Apr 12 at 18:24. show() For an example of the different strategies see: Demonstrating the different strategies of KBinsDiscretizer. gb vp ef tx ch wl dn rs ws vg