In addition to adding the code to allow you to save your image, the code below tries to make the decision tree more interpretable by adding in feature and class names (as well as setting filled = True). The code below plots a decision tree using scikit-learn.
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How to Visualize Decision Trees using MatplotlibĪs of scikit-learn version 21.0 (roughly May 2019), Decision Trees can now be plotted with matplotlib using scikit-learn’s ot_tree without relying on the dot library which is a hard-to-install dependency which we will cover later on in the blog post. # Step 4: Predict labels of unseen (test) data #from ee import DecisionTreeClassifierĬlf = DecisionTreeClassifier(max_depth = 2, # This was already imported earlier in the notebook so commenting out Scikit-learn 4-Step Modeling Pattern # Step 1: Import the model you want to use The colors in the image indicate which variable (X_train, X_test, Y_train, Y_test) the data from the dataframe df went to for a particular train test split The image produced by Michael Galarnyk. X_train, X_test, Y_train, Y_test = train_test_split(df, df, random_state=0) The code below puts 75% of the data into a training set and 25% of the data into a test set.
Splitting Data into Training and Test Sets import pandas as pdįrom sklearn.datasets import load_irisdata = load_iris()ĭf = pd.DataFrame(data.data, columns=data.feature_names) The Iris dataset is one of datasets scikit-learn comes with that do not require the downloading of any file from some external website. import matplotlib.pyplot as pltįrom sklearn.datasets import load_breast_cancerįrom ee import DecisionTreeClassifierįrom sklearn.ensemble import RandomForestClassifierįrom sklearn.model_selection import train_test_split The following import statements are what we will use for this section of the tutorial. If this section is not clear, I encourage you to read my Understanding Decision Trees for Classification (Python) tutorial as I go into a lot of detail on how decision trees work and how to use them. In order to visualize decision trees, we need first need to fit a decision tree model using scikit-learn. With that, let’s get started! How to Fit a Decision Tree Model using Scikit-Learn
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How to Visualize Decision Trees using Graphviz (what is Graphviz, how to install it on Mac and Windows, and how to use it to visualize decision trees).How to Visualize Decision Trees using Matplotlib.How to Fit a Decision Tree Model using Scikit-Learn.Consequently, it would help to know how to make a visualization based on your model. This is not only a powerful way to understand your model, but also to communicate how your model works. Benefits of decision trees include that they can be used for both regression and classification, they don’t require feature scaling, and they are relatively easy to interpret as you can visualize decision trees. Decision trees are a popular supervised learning method for a variety of reasons.
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