How to visualise a tree model Multiclass Classification in python
This recipe helps you visualise a tree model Multiclass Classification in python
Recipe Objective
Visualising a model gives a better representation of how the model is working. Tree models are one of the easiest to visualise.
So this recipe is a short example of how we can visualise a tree model - Multiclass Classification.
Learn to Build a Multi Class Image Classification Model in Python from Scratch
Table of Contents
Step 1 - Import the library
from sklearn import datasets
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
plt.style.use("ggplot")
from sklearn import tree
from sklearn.externals.six import StringIO
import pydotplus
Here we have imported various modules like datasets, StringIO and test_train_split from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.
Step 2 - Setup the Data for classifier
Here we have used datasets to load the inbuilt wine dataset and we have created objects X and y to store the data and the target value respectively.
dataset = datasets.load_wine()
X = dataset.data; y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
Step 3 - Model and its Score
Here, we are using DecisionTreeClassifier as a Machine Learning model to fit the data.
model = tree.DecisionTreeClassifier()
model.fit(X_train, y_train)
print(model)
Now we have predicted the output by passing X_test and also stored real target in expected_y.
expected_y = y_test
predicted_y = model.predict(X_test)
Here we have printed classification report and confusion matrix for the classifier.
print(metrics.classification_report(expected_y, predicted_y,
target_names=dataset.target_names))
print(metrics.confusion_matrix(expected_y, predicted_y))
Step 4 - Visualizing the model
We have created a dot file for the tree and used it to create png and pdf. By using matplotlib we have created a image of the tree with the conditions used by the model.
dotfile = open("tree.dot", "w")
tree.export_graphviz(model, out_file = dotfile, feature_names = dataset.feature_names)
dotfile.close()
dot_data = StringIO()
tree.export_graphviz(model, out_file=dot_data, filled=True, rounded=True,
special_characters=True, feature_names = dataset.feature_names)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png("tree.png")
graph.write_pdf("tree.pdf")
import matplotlib.image as mpimg
img = mpimg.imread("tree.png")
plt.figure(figsize=(15,15))
plt.imshow(img)
plt.show()
As an output we get:
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Ameeruddin Mohammed
I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More