Machine Learning Coding Tutorial 4. Testing Accuracy

Machine Learning Coding Tutorial 4. Testing Accuracy

In this tutorial, we are going to write a program testing machine learning prediction accuracy.

1. Pipeline

First, we need to import Iris data set.

Then we will split data to setup training data and labeling data.

In the example, we will use Decision Tree Classifier and K neighbors classifier to make predictions.

Finally, we compare the label data and prediction to get accuracy.

2. Coding

Let’s head into Python for a programmatic example.

Create a python file and write following code to program.

Please read comments carefully to understand the meaning of codes.

GoodTecher Machine Learning Coding Tutorial

Machine Learning Coding Tutorial 4. Testing Accuracy

The program demonstrate how to calculate machine learning prediction accuracy score

# import Iris dataset
from sklearn import datasets
iris = datasets.load_iris()

# x is data, y is true label
x =
y =

# split half data as test data, half data as training data
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = .5)

# use data to train Decision Tree classifier
from sklearn import tree
tree_classifier = tree.DecisionTreeClassifier(), y_train)

# use data to train kNeighbors classifier
from sklearn.neighbors import KNeighborsClassifier
kNeighbors_classifier = KNeighborsClassifier(), y_train)

# predict
tree_predictions = tree_classifier.predict(x_test)
kNeighbors_predictions = kNeighbors_classifier.predict(x_test)

# compare true labels with prediction values to get accuracy score
from sklearn.metrics import accuracy_score
print (accuracy_score(y_test, tree_predictions))
print (accuracy_score(y_test, kNeighbors_predictions))

Run the program with the following command in Terminal (Mac) or Command Prompt (Windows):



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