www.allitebooks.com

Chapter 2
Finally, we take the last value of the row and set the class. We set it to 1 (or True) if it
is a good sample, and 0 if it is not:
y[i] = row[-1] == 'g'
We now have a dataset of samples and features in X, and the corresponding
classes in y, as we did in the classification example in Chapter 1, Getting Started
with Data Mining.
Moving towards a standard workflow
Estimators in scikit-learn have two main functions: fit() and predict().
We train the algorithm using the fit method and our training set. We evaluate it
using the predict method on our testing set.
First, we need to create these training and testing sets. As before, import and run the
train_test_split function:
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_
state=14)
Then, we import the nearest neighbor class and create an instance for it. We leave
the parameters as defaults for now, and will choose good parameters later in this
chapter. By default, the algorithm will choose the five nearest neighbors to predict
the class of a testing sample:
from sklearn.neighbors import KNeighborsClassifier
estimator = KNeighborsClassifier()
After creating our estimator, we must then fit it on our training dataset. For the
nearest neighbor class, this records our dataset, allowing us to find the nearest
neighbor for a new data point, by comparing that point to the training dataset:
estimator.fit(X_train, y_train)
We then train the algorithm with our test set and evaluate with our testing set:
y_predicted = estimator.predict(X_test)
accuracy = np.mean(y_test == y_predicted) * 100
print("The accuracy is {0:.1f}%".format(accuracy))
This scores 86.4 percent accuracy, which is impressive for a default algorithm and
just a few lines of code! Most scikit-learn default parameters are chosen explicitly
to work well with a range of datasets. However, you should always aim to choose
parameters based on knowledge of the application experiment.
[ 31 ]