For a classification problem using BernoulliNB , how to calculate the joint log-likelihood. The joint likelihood it to be calculated by below formula, where y(d) is the array of actual output (not predicted values) and x(d) is the data set of features.
I read this answer and read the documentation but it didn't exactly served my purpose. Can somebody please
help.
- The solution is to count the positive input variables of the
positive output function.
- We achieve this by counting the
positive output variables or y of index y or y of 1 or y[1] or
data[idx][1].
- The first block of code is the **training** and *learning*.
- The second block of code is the **testing** and *counting*.
- train, test, train_labels, test_labels = train_test_split(Xs[0],
ys[0], test_size=1./3, random_state=r)
naive = BernoulliNB(alpha= 10**-7)
model = naive.fit(train, train_labels)
joint_log_train = model._joint_log_likelihood(train)
- l = [np.append(x,y) for x, y in zip(train, train_labels)]
- # Write your code below this line.
- def count(data, label):
x = 0
for idx, l in enumerate(label):
if (l == True):
x += data[idx][1]
else:
x += data[idx][0]
return x
- for i, (x, y) in enumerate(zip(Xs, ys)):
train, test, train_labels, test_labels = train_test_split(x, y,
test_size=1./3, random_state=r)
for j, a in enumerate(alphas):
naive = BernoulliNB(alpha = a)
model = naive.fit(train, train_labels)
joint_log_train = model._joint_log_likelihood(train)
joint_log_test = model._joint_log_likelihood(test)
train_jil[i][j] = count(joint_log_train, train_labels)
test_jil[i][j] = count(joint_log_test, test_labels)
