human
Solve the following python programming problem given the following code:
# Implementa o classificador naive bayes

class naive_classifier(object):
    def _init_(self):
        self.priors = {}
        self.lh_probs = {}
        self.train = None
          
    def calc_prior_probs(self):
        train = self.train
        classes = train['class'].unique()
        priors = {}
        for class_ in classes:            
            priors[class_] = len(train[train['class'] == class_])/len(train)            
        self.priors = priors
        
    def calc_likelihood_probs(self):
        train = self.train
        columns = carset.drop(['class'],axis=1).columns
        classes = carset['class'].unique() 
        lh_probs = {}
        for column in columns:
            for class_ in classes:
                for cat in carset[column].unique():                    
                    cat_prior_prob = sum(train[column]==cat)/len(train)
                    conditional_prob = sum((train['class']==class_) & (train[column]==cat))/sum(train['class']==class_)                                        
                    
                    if not conditional_prob: conditional_prob = 0.001   
                    
                    lh_probs[column,cat,class_] = conditional_prob/cat_prior_prob      
        self.lh_probs = lh_probs
    
    def fit(self,train):
        self.train = train
        self.calc_prior_probs()
        self.calc_likelihood_probs()
    
    def predict(self,xtest):
        columns = self.train.drop(['class'],axis=1).columns
        classes = self.train['class'].unique()  
        predictions = []
        
        for i in xtest.index:
            posterior_prob = {}
            x = xtest.loc[i]
            for class_ in classes:
                posterior_prob[class_] = 1
                for column in columns: 
                    #cat_prior = sum(self.train[column]==x[column])
                    posterior_prob[class_] *= self.lh_probs[column,x[column],class_]                        
                posterior_prob[class_] *= self.priors[class_]             
           ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Sem valores faltantes. Amém!