![]() The problem is that using Graphviz to convert the dot file into an image file (png, jpg, etc) can be difficult. A dot file is a Graphviz representation of a decision tree. The first part of this process involves creating a dot file. I should note that the reason why I am going over Graphviz after covering Matplotlib is that getting this to work can be difficult. In data science, one use of Graphviz is to visualize decision trees. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. Graphviz is open source graph visualization software. Note that I edited the file to have text colors correspond to whether they are leaf/terminal nodes or decision nodes using a text editor. ot_tree(clf) ĭecision Tree produced through Graphviz. The code below plots a decision tree using scikit-learn. Scikit-learn 4-Step Modeling Pattern # Step 1: Import the model you want to use # This was already imported earlier in the notebook so commenting out #from ee import DecisionTreeClassifier # Step 2: Make an instance of the Model clf = DecisionTreeClassifier(max_depth = 2, random_state = 0) # Step 3: Train the model on the data clf.fit(X_train, Y_train) # Step 4: Predict labels of unseen (test) data # Not doing this step in the tutorial # clf.predict(X_test) 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. 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. import pandas as pd from sklearn.datasets import load_iris data = load_iris() df = pd.DataFrame(data.data, columns=data.feature_names) df = data.target 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 plt from sklearn.datasets import load_iris from sklearn.datasets import load_breast_cancer from ee import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import pandas as pd import numpy as np from sklearn import tree Load the Dataset ![]() 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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