Machine Learning Coding Tutorial 4. Testing Accuracy
In this tutorial, we are going to write a program testing machine learning prediction accuracy.
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.
Let’s head into Python for a programmatic example.
Create a python file pipeline.py and write following code to program.
Please read comments carefully to understand the meaning of codes.
""" GoodTecher Machine Learning Coding Tutorial http://18.104.22.168 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 = iris.data y = iris.target # 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() tree_classifier.fit(x_train, y_train) # use data to train kNeighbors classifier from sklearn.neighbors import KNeighborsClassifier kNeighbors_classifier = KNeighborsClassifier() kNeighbors_classifier.fit(x_train, 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):