Match Naive Bayes to feature types

Understand the idea

Production selects GaussianNB for continuous inputs, BernoulliNB for binary flags and OHE–BernoulliNB for categorical inputs. It does not expose arbitrary mixed inputs or CategoricalNB.

Use each production Naive Bayes preparation path.Scroll the diagram horizontally if needed.

What the syntax does

BernoulliNB(): Uses presence and absence of binary indicators.

Supplied for this practice

These objects are prepared afresh before your editor runs.

train / test: Supplied train; its definition below specifies the values and role. Supplied test; its definition below specifies the values and role.
train, test = train_test_split(df, test_size=0.2, random_state=42, stratify=df.popular)

Outputs you create

answer: The requested result, with the values, labels and row order described by this task.

candy_class · first 8 rows
`competitorname`	`chocolate`	`fruity`	`caramel`	`peanutyalmondy`	`nougat`	`crispedricewafer`	`bar`	`pluribus`	`sugarpercent`	`pricepercent`	`winpercent`	`popular`
100 Grand	1	0	1	0	0	1	1	0	0.732	0.86	66.9717	50% or above
3 Musketeers	1	0	0	0	1	0	1	0	0.604	0.511	67.6029	50% or above
One dime	0	0	0	0	0	0	0	0	0.011	0.116	32.2611	below 50%
One quarter	0	0	0	0	0	0	0	0	0.011	0.511	46.1165	below 50%
Air Heads	0	1	0	0	0	0	0	0	0.906	0.511	52.3415	50% or above
Almond Joy	1	0	0	1	0	0	1	0	0.465	0.767	50.3475	50% or above
Baby Ruth	1	0	1	1	1	0	1	0	0.604	0.767	56.9145	50% or above
Boston Baked Beans	0	0	0	1	0	0	0	1	0.313	0.511	23.4178	below 50%

Your task

Fit BernoulliNB to Candy’s binary flags and predict held-away rows.

answer

Hints

Think: The declared target must stay separate from the presence/absence indicators.

Tools: BernoulliNB and an explicit flags list.

Approach: Fit only the nine permitted flags to the training popular labels and predict the same test schema.

Explained solution

from sklearn.naive_bayes import BernoulliNB
flags=['chocolate','fruity','caramel','peanutyalmondy','nougat','crispedricewafer','hard','bar','pluribus']
model=BernoulliNB().fit(train[flags],train.popular)
answer=model.predict(test[flags])

The binary likelihood matches the feature meanings and excludes the target-derived winpercent source.