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Exploring Naive Bayes

Naive Bayes in Python

Next we will see how we can implement this model in Python. To do so, we will use the scikit-learn library.

To exemplify the implementation of a boosting algorithm for classification we will use the same dataset as for the case of decision trees, random forest and boosting.

Step 1. Reading the processed dataset

In [1]:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y = True, as_frame = True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

X_train.head()
Out[1]:
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
224.63.61.00.2
155.74.41.50.4
656.73.14.41.4
114.83.41.60.2
424.43.21.30.2

The train set will be used to train the model, while the test will be used to evaluate the effectiveness of the model. Furthermore, it is not necessary for the predictor variables to be normalized, since these models are based on Bayes' theorem and make specific assumptions about the distribution of the data, but are not directly affected by the scale of the features.

Step 2: Initialization and training of the model

In [3]:
from sklearn.naive_bayes import GaussianNB

model = GaussianNB()
model.fit(X_train, y_train)
Out[3]:
GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

The training time of a model will depend, first of all, on the size of the dataset (instances and features), and also on the model type and its configuration.

Step 3: Model prediction

Once the model has been trained, it can be used to predict with the test data set.

In [4]:
y_pred = model.predict(X_test)
y_pred
Out[4]:
array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,
       0, 2, 2, 2, 2, 2, 0, 0])

With raw data it is very difficult to know whether the model is getting it right or not. To do this, we must compare it with reality. There are a large number of metrics to measure the effectiveness of a model in predicting, including accuracy, which is the fraction of predictions that the model made correctly.

In [5]:
from sklearn.metrics import accuracy_score

accuracy_score(y_test, y_pred)
Out[5]:
1.0

The model is perfect!

Step 4: Saving the model

Once we have the model we were looking for (presumably after hyperparameter optimization), to be able to use it in the future it is necessary to store it in our directory.

In [6]:
from pickle import dump

dump(model, open("naive_bayes_default.sav", "wb"))

Adding an explanatory name to the model is vital, since in the case of losing the code that has generated it we will know what configuration it has (in this case we say default because we have not customized any of the hyperparameters of the model, we have left the ones that the function has by default).